The regression equation for the relationship between age and autonomy (with the latter as the dependent variable) is autonom = 6.964 + 0.06230age r = 0.28 (a) Explain what 6.964 means. (b) Explain what 0.06230 means. (c) How well does the regression equation fit the data? (d) What is the likely level of autonom for someone aged 54? (e) Using SPSS, how would you generate this regression information?

Why was the variable “# of customer service representatives” dropped from the model?   Write the regression equation.   Sales of men’s clothing (predicted) =   Are the regression coefficients significantly different from 0?   If one mails 10,000 catalogs and has 15 phone lines open, what would the predicted sales of men’s clothing be?   How would you interpret the regression coefficient for number of catalogs mailed?   What is the final R2 of the model? How would you interpret this?   Which of the two independent variables is the most important predictor of the dependent variable? Why?

How does linear regression differ from analysis of variance?

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)…

The coefficient of correlation in a simple regression analysis is = -0.6. The coefficient of determination for this regression would be 0.36 - 0.36 0.6 0.13 O 0.6 or + 0.6

what is the regression equation for all predictors? I

A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a.  For each 1-cm increase in height, weight tends to increase by about 0.32 kg   b.  There is no association between weight and height   c.  Height accounts for about 32% of the total variability in weight   d.  The correlation between weight and height is about 0.32

Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.3 7.4 9.8 9.5 8.8 8.5 Weight (kg) 152 187 286 247 237 231 The regression equation is y =+ (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)

You run a regression analysis on a bivariate set of data (n = 51). You obtain the regression equation y=-4.028r+-18.476 with a correlation coefficient of r=-0.91 (which is significant at a = 0.01). You want to predict what value (on average) for the explanatory variable will give you a value of 170 on the response variable. What is the predicted explanatory value? X = (report 3 decimals)

What is the regression equation? (Round to decimal one) The best predicted weight of a bear with a chest size 43 inches is how many pounds???? Round to decimal one)