A box office analyst seeks to predict opening weekend box office gross for movies. Toward this​ goal, the analyst plans to use online trailer views as a predictor. For each of the 66 ​movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross​ (in millions of​ dollars) are collected and stored in the accompanying table. A linear regression was performed on these​ data, and the result is the linear regre

Write down the fitted regression equation. If in the family, the primary caregiver is the biological mother, whose income is $50,000 and the youth live with the family most of the time. What predicted youth absent days in school?

Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…

Use the Financial database from “Excel Databases.xls” on Blackboard.  Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company.  Use Excel to develop the multiple linear regression model.  Assume a 5% level of significance.   Which independent variable is the strongest predictor of the average P/E ratio of a company?     A. Total Revenues B. Average Yield C. Earnings Per Share D.Return on Equity E. Total Assets F.Dividends Per Share   Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287…

Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion).  Use the horizontal axis for the customer age to graph.  Find the linear regression line that models the data by each type of customer.  Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales.  What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y  / Change in x

A box office analyst seeks to predict opening weekend box office gross for movies. Toward this​ goal, the analyst plans to use online trailer views as a predictor. For each of the 66 ​movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross​ (in millions of​ dollars) are collected and stored in the accompanying table. A linear regression was performed on these​ data, and the result is the linear regression equation Yi=−0.840+1.4108Xi. Determine the coefficient of​ determination,r2​,and interpret its meaning. Determine the standard error of the estimate. How useful do you think this regression model is for predicting opening weekend box office​ gross? Can you think of other variables that might explain the variation in opening weekend box office​ gross?

Write out the full linear model including all dummy variables below. Don’t worry about estimating regression coefficients just yet. Feel free to abbreviate variable names so long as they are clearly distinguishable.

A box office analyst seeks to predict opening weekend box office gross for movies. Toward this​ goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross​ (in millions of​ dollars) are collected and stored in the accompanying table. A linear regression was performed on these​ data, and the result is the linear regression equation Yi=−1.254+1.3968Xi.   Complete parts​ (a) through​ (d). a. Determine the coefficient of​ determination,r2​,and interpret its meaning. b. Determine the standard error of the estimate.   c. How useful do you think this regression model is for predicting opening weekend box office​ gross?   d. Can you think of other variables that might explain the variation in opening weekend box office​ gross?

An online retailer examined their transactional database to see how the value of total annual purchases ($) for individual customers was related to their annual income ($). They obtained the following regression model:Total annual purchases = -49.80 + 0.0246(annual income)correlation = 0.533(a) What does the slope tell us? (Annual Income/ Total Annual Purchases) is/are predicted to increase by ($0.0246 / -$49.80) for each additional $1 of (Annual Income/ Total Annual Purchases).(b) What does the intercept tell us? A.The intercept does not have a practical meaning in this context. B. Total annual purchases are predicted to be $0.0246 when annual income = 0.     C. Total annual purchases are predicted to be $-49.80 when annual income = 0. D. Annual income is predicted to be $0.0246 when total annual purchases = 0. E. Annual income is predicted to be $-49.80 when total annual purchases = 0. (c) Which statement is correct concerning the quality of this model? A. 28.4% of the variability in…

A social scientist collects information about counties in California and finds that the correlation between average income of the county and a rating of healthcare quality in the county is 0.78. A scatterplot of the two variables is football shaped. A particular county has an average income that is 0.4 SDs above the average of all counties. Using regression, we would predict that its healthcare quality is _________ SDs above the average healthcare quality for all counties, and that it is therefore at the _________ percentile of healthcare quality among all counties. Choose the answer below to fill in the two blanks. Group of answer choices 0.31; 24th 0.78; 58th 0.78; 79th 0.31; 62nd      PreviousNext