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27. The regression analysis below characterizes...

27.

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variable "average monthly temperature" (in degrees Fahrenheit).

A reasonably accurate prediction would be that:

Ice Cream Consumption and Temperature

Source

•

If the average monthly temperature is 48 degrees, average per household ice cream consumption will be around 3.1 pints.

•

If average monthly temperature is 48 degrees, average per household ice cream consumption will be around 2.6 pints.

•

If average monthly temperature is 48 degrees, average per household ice cream consumption will be around 86.3 pints.

•

If average monthly temperature is 48 degrees, average per household ice cream consumption will be around 1.3 pints.

28.

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variable "average monthly temperature" (in degrees Fahrenheit). The Multiple R value, 0.8409 tells us the correlation between the dependent and independent variables. The R-squared value is 71%.

Based on these measures, we can conclude that:

Ice Cream Consumption and Temperature

Source

•

The variation in average monthly temperature explains about 71% of the variation in household ice cream consumption.

•

The variation in average monthly temperature explains about 84% of the variation in household ice cream consumption.

•

Predictions about future household ice cream sales will be correct within a 71% confidence interval.

•

Predictions about future household ice cream sales will be correct within an 84% confidence interval

29.

The scatter diagram and regression line below characterize the relationship between monthly ice cream consumption (in pints per household) and the independent variable "average monthly temperature" (in degrees Fahrenheit).

The residual errors are defined as:

Ice Cream Consumption and Temperature

Source

•

The vertical distances from the data points to the horizontal axis.

•

The horizontal distances from the data points to the vertical axis.

•

The vertical distances from the data points to the regression line.

•

The shortest distance from the data points to the regression line

30.

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variables "average monthly temperature" (in degrees Fahrenheit) and "average price" (in dollars per pint).

At a significance level of 0.05, which independent variables contribute significantly to the consumption of ice cream?

Ice Cream Consumption, Temperature and Price

Source

•

Average monthly temperature only.

•

Average price of a pint of ice cream only.

•

Both independent variables.

•

Neither independent variable.

31.

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variables "average monthly temperature" (in degrees Fahrenheit) and "average price" (in dollars per pint).

The coefficient of 0.027 for average monthly temperature informs us about:

Ice Cream Consumption, Temperature and Price

Source

•

The relationship between temperature and ice cream consumption when not controlling for price (i.e. allowing price to range freely).

•

The relationship between temperature and ice cream consumption when controlling for price (i.e. fixing price).

•

The relationship between temperature and ice cream price when controlling for consumption (i.e. fixing consumption).

•

The relationship between temperature and ice cream price when not controlling for consumption (i.e. allowing consumption to range freely).

32.

The regression analysis below characterizes the relationship between the current month's ice cream consumption (in pints per household) and the independent variables "current month's average temperature" (in degrees Fahrenheit), "previous month's average temperature" (in degrees Fahrenheit), and "average price" (in dollars per pint).

In this analysis, the variable "previous month's temperature" is:

Ice Cream Consumption, Two Months Temperatures

and Price

Source

•

A lagged variable.

•

A dependent variable.

•

Not statistically significant at the 0.05 level.

•

All of the above.

33.

The regression analysis at the bottom characterizes the relationship between the daily number of visitors at an amusement park and the variable "school day." "School day" is an independent variable that is equal to 1 when a given day is a school day and equal to 0 when it is not.

The coefficient of -13,644 tells us that:

Amusement Park Visitors

Source

•

On average, the number of visitors on school days is 13,644 lower than on non-school days.

•

On every school day, the number of visitors is exactly 13,644 lower than on non-school days.

•

On average, the number of visitors on school days is 13,644.

•

On average, the number of visitors on non-school days is 13,644

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variable "average monthly temperature" (in degrees Fahrenheit).

A reasonably accurate prediction would be that:

Ice Cream Consumption and Temperature

Source

•

If the average monthly temperature is 48 degrees, average per household ice cream consumption will be around 3.1 pints.

•

If average monthly temperature is 48 degrees, average per household ice cream consumption will be around 2.6 pints.

•

If average monthly temperature is 48 degrees, average per household ice cream consumption will be around 86.3 pints.

•

If average monthly temperature is 48 degrees, average per household ice cream consumption will be around 1.3 pints.

28.

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variable "average monthly temperature" (in degrees Fahrenheit). The Multiple R value, 0.8409 tells us the correlation between the dependent and independent variables. The R-squared value is 71%.

Based on these measures, we can conclude that:

Ice Cream Consumption and Temperature

Source

•

The variation in average monthly temperature explains about 71% of the variation in household ice cream consumption.

•

The variation in average monthly temperature explains about 84% of the variation in household ice cream consumption.

•

Predictions about future household ice cream sales will be correct within a 71% confidence interval.

•

Predictions about future household ice cream sales will be correct within an 84% confidence interval

29.

The scatter diagram and regression line below characterize the relationship between monthly ice cream consumption (in pints per household) and the independent variable "average monthly temperature" (in degrees Fahrenheit).

The residual errors are defined as:

Ice Cream Consumption and Temperature

Source

•

The vertical distances from the data points to the horizontal axis.

•

The horizontal distances from the data points to the vertical axis.

•

The vertical distances from the data points to the regression line.

•

The shortest distance from the data points to the regression line

30.

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variables "average monthly temperature" (in degrees Fahrenheit) and "average price" (in dollars per pint).

At a significance level of 0.05, which independent variables contribute significantly to the consumption of ice cream?

Ice Cream Consumption, Temperature and Price

Source

•

Average monthly temperature only.

•

Average price of a pint of ice cream only.

•

Both independent variables.

•

Neither independent variable.

31.

The regression analysis below characterizes the relationship between monthly ice cream consumption (in pints per household) and the independent variables "average monthly temperature" (in degrees Fahrenheit) and "average price" (in dollars per pint).

The coefficient of 0.027 for average monthly temperature informs us about:

Ice Cream Consumption, Temperature and Price

Source

•

The relationship between temperature and ice cream consumption when not controlling for price (i.e. allowing price to range freely).

•

The relationship between temperature and ice cream consumption when controlling for price (i.e. fixing price).

•

The relationship between temperature and ice cream price when controlling for consumption (i.e. fixing consumption).

•

The relationship between temperature and ice cream price when not controlling for consumption (i.e. allowing consumption to range freely).

32.

The regression analysis below characterizes the relationship between the current month's ice cream consumption (in pints per household) and the independent variables "current month's average temperature" (in degrees Fahrenheit), "previous month's average temperature" (in degrees Fahrenheit), and "average price" (in dollars per pint).

In this analysis, the variable "previous month's temperature" is:

Ice Cream Consumption, Two Months Temperatures

and Price

Source

•

A lagged variable.

•

A dependent variable.

•

Not statistically significant at the 0.05 level.

•

All of the above.

33.

The regression analysis at the bottom characterizes the relationship between the daily number of visitors at an amusement park and the variable "school day." "School day" is an independent variable that is equal to 1 when a given day is a school day and equal to 0 when it is not.

The coefficient of -13,644 tells us that:

Amusement Park Visitors

Source

•

On average, the number of visitors on school days is 13,644 lower than on non-school days.

•

On every school day, the number of visitors is exactly 13,644 lower than on non-school days.

•

On average, the number of visitors on school days is 13,644.

•

On average, the number of visitors on non-school days is 13,644

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