Regression analysis was applied between sales data (y) and advertising data (x) and the following information was obtained. SSR = 800 SST = 1250 Sample Size (n) = 12 The standard error of the estimate is

Draw the regression line for the following equation: Y = -3 + (1) X Show your calculation for a low and high value on the predictor value.

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=ax+b a=-0.609 b=27.646 r2=0.910116 r=-0.954 Use this to predict the number of situps a person who watches 12 hours of TV can do (to one decimal place)

Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable.  Regression Statistics           Multiple R 0.963           R Square             Adjusted R Square             Standard Error             Observations 20                         ANOVA               df SS MS F Significance F Regression   24686354.49 6171589 47.8 2.3289E-08   Residual     129243       Total   26625000…

Increase in sales (percent) An advertising firm wishes to demonstrate to its clients the effectiveness of the advertising campaigns it has conducted. The following bivariate data on twelve recent campaigns, including the cost of each campaign (in millions of dollars) and the resulting percentage increase in sales following the campaign, were presented by the firm. Based on these data, we would compute the least-squares regression line to be y = 6.16+0.18x, with x representing campaign cost and y representing the resulting percentage increase in sales. (This line is shown below, along with a scatter plot of the data.) Increase in sales, y Campaign cost, x (in millions of dollars) (percent) 3.02 6.91 7.2+ 1.92 6.80 3.83 6.85 6.8- 1.40 6.37 6.6 - 3.12 6.42 3.56 6.82 6.4- 4.06 6.94 6.2 1.64 6.56 6- 2.06 6.50 1.62 6.18 1.5 2.5 3.5 6.66 Campaign cost (in millions of dollars) 2.87 2.25 6.61 Send data to calculator Based on the firm's data and the regression line, complete the following. (a)…

A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-1.364 b=32.282 r2=0.398161 r=-0.631 Use this to predict the number of situps a person who watches 4 hour(s) of TV can do, and please round your answer to a whole number.

A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.013 b=27.906 r2=0.498436 r=-0.706 Use this to predict the number of sit ups a person who watches 12 hours of TV can do, and please round your answer to a whole number.

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.371 b=36.465 ²0.395641 r=-0.629 Use this to predict the number of situps a person who watches 9 hours of TV can do (to one decimal place)

A local retail store compared their monthly sales of umbrellas with the amount of rainfall that occured during that month. They computed the following statistics: Rainfall (in) # of umbrellas mean = 4.64 mean = 34.2 SD = 1.17 SD = 13.2 r = 0.8 1. Find the equation for the regression line that predicts the monthly sales of umbrellas from monthly rainfall.

The regression line for the given data is  = 6.91x + 46.26. Determine the residual of a data point for which x = 4 and y = 75.