2753 Trend Lines & Forecasting (No Seasonality)
- 1.
Using the data set above (TS7E8), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Explanation
The equation y=10.38+0.13x represents the trend line for the given data set TS7E8. The equation suggests that there is a linear relationship between the dependent variable y and the independent variable x. The slope of the line is 0.13, indicating that for each unit increase in x, y increases by 0.13 units. The y-intercept is 10.38, which represents the expected value of y when x is equal to zero. Therefore, this equation accurately represents the trend line for the given data set.Rate this question:
- 2.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that need to be deseasonalised
- C.
No, it appears to have cyclic variation that needs to be randomised
- D.
Yes, it appears to have cyclic variation
Correct Answer
B. No, it appears to have seasonal variation that need to be deseasonalised -
- 3.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation that needs to be randomised
- D.
Yes, it appears to have cyclic variation
Correct Answer
B. No, it appears to have seasonal variation that needs to be deseasonalisedExplanation
The correct answer is No, it appears to have seasonal variation that needs to be deseasonalised. This is because the time series plot shows a repeating pattern or seasonality, indicating that there are regular fluctuations in the data over a specific time period. To analyze the underlying trend in the data, it is necessary to remove the seasonal component through deseasonalization techniques.Rate this question:
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- 4.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
B. No, it appears to have seasonal variation that needs to be deseasonalisedExplanation
Based on the given information, the time series plot shows that there is seasonal variation in the data. Therefore, it is recommended to apply a trend line to the raw data in order to deseasonalize it and remove the seasonal variation.Rate this question:
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- 5.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
B. No, it appears to have seasonal variation that needs to be deseasonalisedExplanation
Based on the information given, the time series plot shows that there is seasonal variation in the data. Therefore, applying a trend line to the raw data would not be appropriate. Instead, the data should be deseasonalized to remove the seasonal component before analyzing any trends.Rate this question:
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- 6.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
C. No, it appears to have cyclic variationExplanation
The correct answer is "No, it appears to have cyclic variation." This is because the time series plot shows patterns that repeat at regular intervals, indicating the presence of cyclic variation. Therefore, applying a trend line to the raw data would not be appropriate as it would not capture the cyclic component of the data.Rate this question:
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- 7.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
C. No, it appears to have cyclic variationExplanation
Based on the information provided, it can be inferred that the raw data does not exhibit a clear trend over time. Instead, it appears to have cyclic variation, meaning that there are repeated patterns or cycles in the data. Therefore, applying a trend line to the raw data would not be appropriate in this case.Rate this question:
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- 8.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
C. No, it appears to have cyclic variationExplanation
The correct answer is No, it appears to have cyclic variation. This is because the time series plot shows a pattern that repeats over a certain period of time, indicating the presence of cyclic variation.Rate this question:
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- 9.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. Yes, it appears to have random variation only.Explanation
Based on the given time series plot, it can be observed that there is no clear pattern or trend in the data. The data points seem to fluctuate randomly without any consistent upward or downward movement. Therefore, applying a trend line to the raw data would not be necessary as it would not capture any meaningful trend.Rate this question:
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- 10.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. Yes, it appears to have random variation only.Explanation
The correct answer is "Yes, it appears to have random variation only." This can be inferred from the time series plot, which shows that there is no clear pattern or trend in the data. The data points seem to fluctuate randomly around a mean value, indicating that there is no systematic increase or decrease over time. Therefore, applying a trend line to the raw data would not be appropriate in this case.Rate this question:
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- 11.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. Yes, it appears to have random variation only.Explanation
The correct answer is Yes, it appears to have random variation only. Based on the information provided, the time series plot does not show any clear patterns or trends over time. Therefore, applying a trend line to the raw data would not be appropriate as it would not capture any meaningful information.Rate this question:
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- 12.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. Yes, it appears to have random variation only.Explanation
Based on the information given, the correct answer is "Yes, it appears to have random variation only." This is because the time series plot does not show any clear patterns or trends. The data points appear to be scattered randomly without any noticeable upward or downward trend. Therefore, applying a trend line to the raw data would not be necessary as there is no evidence of any systematic or predictable variation in the data.Rate this question:
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- 13.
From the time series plot above, should you apply a trend line to the raw data?
- A.
Yes, it appears to have random variation only.
- B.
No, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. Yes, it appears to have random variation only.Explanation
Based on the given time series plot, it can be observed that there is no clear trend or pattern in the data. The data points seem to fluctuate randomly without any consistent upward or downward movement. Therefore, it is appropriate to apply a trend line to the raw data in order to identify any underlying trend or pattern.Rate this question:
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- 14.
Using the data set above (TS751), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
y=2173.74-12.95xExplanation
The equation for the trend line is y=2173.74-12.95x. This equation represents a linear relationship between the dependent variable y and the independent variable x. The slope of the line is -12.95, indicating that for every unit increase in x, y decreases by 12.95 units. The y-intercept of the line is 2173.74, which represents the value of y when x is equal to zero. This equation suggests that there is a negative relationship between x and y, and it can be used to make predictions or estimate values of y based on different values of x.Rate this question:
- 15.
Using the data set above (TS752), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
y=837.99+32.07xExplanation
The equation y=837.99+32.07x represents a linear trend line. It suggests that there is a positive relationship between the dependent variable (y) and the independent variable (x). For every unit increase in x, the value of y will increase by 32.07. The y-intercept is 837.99, indicating that when x is zero, the predicted value of y is 837.99. This equation is suitable for creating a trend line for the given data set.Rate this question:
- 16.
Using the data set above (TS7E1), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
y=520.4+10.09xExplanation
The equation for the trend line is y=520.4+10.09x. This equation represents a linear relationship between the dependent variable y and the independent variable x. The constant term 520.4 represents the y-intercept, which is the value of y when x is 0. The coefficient 10.09 represents the slope of the line, indicating the rate of change in y for each unit increase in x. This equation suggests that as x increases, y increases at a rate of 10.09 units. Therefore, the given data set is suited to a trend line.Rate this question:
- 17.
Using the data set above (TS7E3), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
y=17.59-0.40xExplanation
The given equation y=17.59-0.40x represents the equation for the trend line. It shows that the value of y is determined by the value of x, with a slope of -0.40. This means that for every increase of 1 in x, y will decrease by 0.40. The y-intercept is 17.59, indicating that when x is 0, y will be 17.59. This equation suggests that there is a negative linear relationship between x and y, where y decreases as x increases.Rate this question:
- 18.
Using the data set above (TS7E4), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
y=12.47-0.26xExplanation
The equation for the trend line is y=12.47-0.26x. This equation represents a linear relationship between the variables x and y, where y decreases by 0.26 units for each unit increase in x. The y-intercept is 12.47, indicating that when x is 0, y is 12.47. This equation suggests that there is a negative correlation between x and y, and the trend line can be used to predict y values for different x values.Rate this question:
- 19.
Using the data set above (TS7E5), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
y=27.20+0.20xExplanation
The equation given represents the trend line for the data set. It is in the form of y = mx + b, where m is the slope of the line (0.20) and b is the y-intercept (27.20). This equation can be used to predict the value of y for any given value of x in the data set.Rate this question:
- 20.
Using the data set above (TS7E6), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
noneExplanation
The given answer "none" indicates that the data set (TS7E6) is not suited to a trend line. This means that there is no linear relationship or pattern that can be observed in the data, and therefore, it is not possible to create an equation for a trend line.Rate this question:
- 21.
Using the data set above (TS7E7), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
none - 22.
Using the data set above (TS7E8), write the equation for the trend line. (2 decimal places,no spaces) If the data is not suited to a trend line type 'none'
Correct Answer
y=10.38+0.13xExplanation
The equation for the trend line is y=10.38+0.13x. This equation represents a linear relationship between the dependent variable y and the independent variable x. The coefficient 10.38 represents the y-intercept, which is the value of y when x is 0. The coefficient 0.13 represents the slope of the line, indicating the rate of change of y with respect to x. This equation suggests that as x increases by 1 unit, y will increase by 0.13 units. Therefore, the given equation is suitable for representing the trend in the data set.Rate this question:
- 23.
The data below shows the number of students (in thousands) enrolled in university each year from 1992 to 2001. Using the data set above (TS7E1), predict the number of students (to the nearest 1000) enrolled in 2002. Use an equation correct to 2 decimal places.
Correct Answer
631000, 631,000 - 24.
The data below shows the number of students (in thousands) enrolled in university each year from 1992 to 2001. Using the data set above (TS7E1), predict the number of students (to the nearest 1000) enrolled in 2003. Use an equation correct to 2 decimal places.
Correct Answer
641000, 641,000 - 25.
The data below shows the number of students (in thousands) enrolled in university each year from 1992 to 2001. Using the data set above (TS7E1), predict the number of students (to the nearest 1000) enrolled in 2008. Use an equation correct to 2 decimal places.
Correct Answer
692000, 692,000 - 26.
The data below shows the number of students (in thousands) enrolled in university each year from 1992 to 2001. Using the data set above (TS7E1), predict the number of students (to the nearest 1000) enrolled in 2009. Use an equation correct to 2 decimal places.
Correct Answer
702000, 702,000Explanation
The given answer is 702,000. This is because the question asks to predict the number of students enrolled in 2009 using the given data set. However, the data set provided only includes information from 1992 to 2001, so there is no data available for 2009. Therefore, the answer is that the prediction cannot be made based on the given data.Rate this question:
- 27.
The data below shows the number of students (in thousands) enrolled in university each year from 1992 to 2001. Using the data set above (TS7E1), predict the number of students (to the nearest 1000) enrolled in 2010. Use an equation correct to 2 decimal places.
Correct Answer
712000, 712,000 - 28.
The data below shows the average number of questions asked by members of parliament during Question Time for the period 1976 to 1992. Using the data set above (TS7E3), create a trend line correct to 2 decimal places, and forecast the average number of questions asked in 1993. (nearest whole number)
Correct Answer
10 - 29.
The data below shows the average number of questions asked by members of parliament during Question Time for the period 1976 to 1992. Using the data set above (TS7E3), create a trend line correct to 2 decimal places, and forecast the average number of questions asked in 1995. (nearest whole number)
Correct Answer
10 - 30.
The data below shows the average number of questions asked by members of parliament during Question Time for the period 1976 to 1992. Using the data set above (TS7E3), create a trend line correct to 2 decimal places, and forecast the average number of questions asked in 1997. (nearest whole number)
Correct Answer
9 - 31.
The data below shows the average number of questions asked by members of parliament during Question Time for the period 1976 to 1992. Using the data set above (TS7E3), create a trend line correct to 2 decimal places, and forecast the average number of questions asked in 1999. (nearest whole number)
Correct Answer
8 - 32.
The data below shows the average number of questions asked by members of parliament during Question Time for the period 1976 to 1992. Using the data set above (TS7E3), create a trend line correct to 2 decimal places, and forecast the average number of questions asked in 2001. (nearest whole number)
Correct Answer
7 - 33.
The data below shows the average number of questions asked by members of parliament during Question Time for the period 1976 to 1992. Using the data set above (TS7E3), create a trend line correct to 2 decimal places, and forecast the average number of questions asked in 2010. (nearest whole number)
Correct Answer
4 - 34.
The data below shows the average age of first time mothers in Australia from 1989 to 2002. Using the data set above (TS7E5), create a trend line correct to 2 decimal places, and forecast the average age of first time mothers in 2018. (1 decimal place)
Correct Answer
33.2 - 35.
The data below shows the average age of first time mothers in Australia from 1989 to 2002. Using the data set above (TS7E5), create a trend line correct to 2 decimal places, and forecast the average age of first time mothers in 2003. (1 decimal place)
Correct Answer
30.2Explanation
Based on the given data set, a trend line was created to represent the average age of first time mothers in Australia from 1989 to 2002. The trend line was then used to forecast the average age of first time mothers in 2003, and the result obtained was 30.2.Rate this question:
- 36.
The data below shows the average age of first time mothers in Australia from 1989 to 2002. Using the data set above (TS7E5), create a trend line correct to 2 decimal places, and forecast the average age of first time mothers in 2005. (1 decimal place)
Correct Answer
30.6 - 37.
The data below shows the average age of first time mothers in Australia from 1989 to 2002. Using the data set above (TS7E5), create a trend line correct to 2 decimal places, and forecast the average age of first time mothers in 2007. (1 decimal place)
Correct Answer
31, 31.0Explanation
The data provided shows that the average age of first-time mothers in Australia remained constant at 31.0 from 1989 to 2002. Therefore, it can be assumed that the trend line for this data would also be a horizontal line at 31.0. Based on this trend, it can be forecasted that the average age of first-time mothers in 2007 would also be 31.0.Rate this question:
- 38.
The data below shows the average age of first time mothers in Australia from 1989 to 2002. Using the data set above (TS7E5), create a trend line correct to 2 decimal places, and forecast the average age of first time mothers in 2009. (1 decimal place)
Correct Answer
31.4 - 39.
The data below shows the average age of first time mothers in Australia from 1989 to 2002. Using the data set above (TS7E5), create a trend line correct to 2 decimal places, and forecast the average age of first time mothers in 2013. (1 decimal place)
Correct Answer
32.2 - 40.
A trend line is calculated for a time series plot : birth rate = 10.5 - 2 x Coded Month Interpret the gradient.
- A.
For every month of coded time, the birth rate will increase by 2
- B.
For every 10.5 months, births increase by 2
- C.
For every month of coded time, the birth rate will decrease by 2
- D.
For every month of coded time, the birth rate will decrease by 10.5
- E.
For every month of coded time, the birth rate will increase by 10.5
Correct Answer
C. For every month of coded time, the birth rate will decrease by 2Explanation
For every month of coded time, the birth rate will decrease by 2. This means that as the coded time increases, the birth rate will decrease at a rate of 2 units per month.Rate this question:
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- 41.
A trend line is calculated for a time series plot : birth rate = 27.5 + 5 x Coded Year Interpret the gradient.
- A.
For every year of coded time, the birth rate will increase by 5
- B.
For every 27.5 years, births increase by 5
- C.
For every year of coded time, the birth rate will decrease by 5
- D.
For every year of coded time, the birth rate will decrease by 27.5
- E.
For every year of coded time, the birth rate will increase by 27.5
Correct Answer
A. For every year of coded time, the birth rate will increase by 5Explanation
The gradient of the trend line represents the rate of change in the birth rate for each unit increase in the coded year. In this case, the gradient is 5, indicating that for every year of coded time, the birth rate will increase by 5.Rate this question:
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- 42.
A trend line is calculated for a time series plot : birth rate = 15 + 7 x Coded Week Interpret the gradient.
- A.
For every year of coded time, the birth rate will increase by 7
- B.
For every 15 years, births increase by 7
- C.
For every week of coded time, the birth rate will increase by 7
- D.
For every week of coded time, the birth rate will decrease by 15
- E.
For every week of coded time, the birth rate will increase by 15
Correct Answer
C. For every week of coded time, the birth rate will increase by 7Explanation
The gradient in this context refers to the rate of change of the birth rate with respect to the coded time. The given equation shows that for every week of coded time, the birth rate will increase by 7. This means that as the coded time increases by one week, the birth rate will increase by 7 units.Rate this question:
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- 43.
A trend line is calculated for a time series plot : birth rate = 25 + 4 x Coded Week Interpret the gradient.
- A.
For every year of coded time, the birth rate will increase by 4
- B.
For every 25 years, births increase by 4
- C.
For every week of coded time, the birth rate will increase by 4
- D.
For every week of coded time, the birth rate will decrease by 25
- E.
For every week of coded time, the birth rate will increase by 25
Correct Answer
C. For every week of coded time, the birth rate will increase by 4Explanation
The gradient in this context refers to the coefficient of the coded week variable in the trend line equation. Since the coefficient is 4, it means that for every week of coded time, the birth rate will increase by 4.Rate this question:
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- 44.
A trend line is calculated for a time series plot : birth rate = 25 -1.2 x Coded Week Interpret the gradient.
- A.
For every year of coded time, the birth rate will decrease by 1.2
- B.
For every 25 years, births increase by 1.2
- C.
For every week of coded time, the birth rate will increase by 1.2
- D.
For every week of coded time, the birth rate will decrease by 1.2
- E.
For every week of coded time, the birth rate will increase by 25
Correct Answer
D. For every week of coded time, the birth rate will decrease by 1.2Explanation
The gradient in this context refers to the coefficient of the coded week variable (-1.2). A negative coefficient indicates a negative relationship between the coded week and the birth rate. Therefore, for every week of coded time, the birth rate will decrease by 1.2.Rate this question:
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- 45.
A trend line is calculated for a time series plot : student attendance = 86 - 1.2 x Coded Month Interpret the gradient.
- A.
For every year of coded time, the student attendance will increase by 1.2
- B.
For every 86 months of coded time, student attendance will decrease by 1.2
- C.
For every month of coded time, student attendance will increase by 1.2
- D.
For every month of coded time, student attendance will decrease by 1.2
- E.
For every week of coded time, student attendance will decrease by 86
Correct Answer
D. For every month of coded time, student attendance will decrease by 1.2Explanation
The gradient in this context refers to the coefficient or slope of the coded month variable in the trend line equation. The equation states that for every unit increase in coded month, the student attendance will decrease by 1.2. Therefore, the correct interpretation of the gradient is that for every month of coded time, student attendance will decrease by 1.2.Rate this question:
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- 46.
A trend line is calculated for a time series plot : student attendance = 76 - 1.3 x Coded Month Interpret the gradient.
- A.
For every year of coded time, the student attendance will decrease by 1.3
- B.
For every 76months of coded time, student attendance will decrease by 1.3
- C.
For every month of coded time, student attendance will increase by 1.3
- D.
For every month of coded time, student attendance will decrease by 1.3
- E.
For every week of coded time, student attendance will decrease by 76
Correct Answer
D. For every month of coded time, student attendance will decrease by 1.3Explanation
The gradient in this context refers to the coefficient of the coded month variable in the equation. Since the coefficient is negative (-1.3), it indicates that as the coded month increases by 1, the student attendance will decrease by 1.3. Therefore, for every month of coded time, student attendance will decrease by 1.3.Rate this question:
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- 47.
A trend line is calculated for a time series plot : student attendance = 86 - 0.5 x Coded Week Interpret the gradient.
- A.
For every month of coded time, the student attendance will decrease by 0.5
- B.
For every 86 weeks of coded time, student attendance will decrease by 0.5
- C.
For every week of coded time, student attendance will increase by 0.5
- D.
For every week of coded time, student attendance will decrease by 0.5
- E.
For every week of coded time, student attendance will decrease by 76
Correct Answer
D. For every week of coded time, student attendance will decrease by 0.5Explanation
The gradient of -0.5 indicates that for every week of coded time, the student attendance will decrease by 0.5. This means that as the coded time increases, the student attendance will gradually decrease at a rate of 0.5 per week.Rate this question:
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- 48.
A trend line is calculated for a time series plot : student attendance = 96 - 0.8 x Coded Week Interpret the gradient.
- A.
For every month of coded time, the student attendance will decrease by 0.8
- B.
For every 96 weeks of coded time, student attendance will decrease by 0.8
- C.
For every week of coded time, student attendance will increase by 0.8
- D.
For every week of coded time, student attendance will decrease by 0.8
- E.
For every week of coded time, student attendance will decrease by 96
Correct Answer
D. For every week of coded time, student attendance will decrease by 0.8Explanation
The gradient in this context refers to the change in student attendance for each unit of coded time. Since the coefficient of the coded week is -0.8, it indicates that for every week of coded time, student attendance will decrease by 0.8. This means that as the coded time increases, the student attendance will gradually decrease.Rate this question:
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- 49.
A trend line is calculated for a time series plot : student attendance = 96 - 0.8 x Coded Day Interpret the gradient.
- A.
For every week of coded time, the student attendance will decrease by 0.8
- B.
For every 96 days of coded time, student attendance will decrease by 0.8
- C.
For every day of coded time, student attendance will increase by 0.8
- D.
For every day of coded time, student attendance will decrease by 0.8
- E.
For every day of coded time, student attendance will decrease by 96
Correct Answer
D. For every day of coded time, student attendance will decrease by 0.8Explanation
The gradient of the trend line represents the rate of change of the dependent variable (student attendance) with respect to the independent variable (coded time). In this case, the gradient is -0.8, indicating that for every day of coded time, student attendance will decrease by 0.8.Rate this question:
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- 50.
A trend line is calculated for a time series plot : student attendance = 65 + 1.1 x Coded Day Interpret the gradient.
- A.
For every week of coded time, the student attendance will decrease by 1.1
- B.
For every 65 days of coded time, student attendance will decrease by 1.1
- C.
For every day of coded time, student attendance will increase by 1.1
- D.
For every day of coded time, student attendance will decrease by 1.1
- E.
For every day of coded time, student attendance will decrease by 65
Correct Answer
C. For every day of coded time, student attendance will increase by 1.1 -
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Mar 19, 2023Quiz Edited by
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Apr 26, 2014Quiz Created by
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