Mumbai Electronics is planning to extend its marketing region from the western United States to include the midwestern states. In order to predict its sales in this new region, the company has asked you to develop a linear regression of DVD system sales on price, using the following data supplied by the marketing department: Sales 418 384 343 407 432 386 444 427 Price  98  194  231 207 89 255 149 195 a. Use an unbiased estimation procedure to find an estimate of the variance of the error terms in the population regression. b. Use an unbiased estimation procedure to find an estimate of the variance of the least squares estimator of the slope of the population regression line. c. Find a 90% confidence interval for the slope of the population regression line.

Listed. below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $115 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 41 21 114 68 79 51 118 65 9. 61 122 21 15 151 110 119 11 107 56 128 116 101 107 55 105 222 39 22 283 45 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y%= X. %3D (Round to one decimal place as needed.)

Brandon works as a statistician for the Toronto Blue Jays, and wants to analyze the relationship between a player's age and how many strikeouts they accumulate in a season. He takes a sample of 8 Blue Jays players with age between 25 and 34 and finds there is a linear relationship between their ages and the number of strikeouts they had in the 2015 season. Here are the numerical summaries for age and the number of strikeouts: r = 0.67, age = 28.4, Sage = 3.96, strikeout= 102.9, S strikeout = 7.7 (a) What is the value of b₁, i.e. the fitted slope? (Round your answer to 3 decimal places) Answer: (b) What the value of bo, i.e. the fitted intercept? (Round your answer to 3 decimal places.) Answer: (c) What is the percent of variation of the number of strikeouts that is explained by age using a linear regression? (Round your answer to 2 decimal places.) Answer: % (d) Can we use this linear regression to predict the number of strikeouts for a player age 38? Answer: O No, because the…

The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…

The scatterplot below shows the relationship between poverty rate in the 51 states in the US (including DC) and high school graduation rate. The linear for predicting poverty is as follows: poverty = 64.68 -0.62 HS-grad-rate High school graduation rate for North Carolina is 81.4% and the poverty rate is 13.1%. What is the residual for this observation? Choose the closest answer. % in poverty 18 16- 14 12 10 8 6 1.1 O-24.8 24.8 -1.1 80 85 % HS grad 90

Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables?    (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly.     (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly.       (d) Suppose a second regression model (Model 2) was generated using only…

Hiroshi Sato, an owner of a sushi restaurant in San Francisco, has been following an aggressive marketing campaign to thwart the effect of rising unemployment rates on business. He used monthly data on sales ($1,000s), advertising costs ($), and the unemployment rate (%) fromJanuary 2008 to May 2009 to estimate the following sample regression equation: Sales(t) = 17.51 +0.05 Advertising Costs(t-1) – 0.70 Unemployment Rate t-1 Requirement: a. Hiroshi had budgeted $620 toward advertising costs in May 2009. Make a forecast in June2009, if the unemployment rate in May 2009 was 9.1%b. What will be the forecast if he raises his advertisement budget to $700?c. Reevaluate the above forecast if the unemployment rate in May 2009 was 9.5%.    Please, if possible, can you do the answer in Excel/Spreadsheet

The owner of Showtime Movie Theaters, Inc. would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow.  Weekly GrossRevenue($1000s) TelevisonAdvertising($1000s) NewspaperAdvertising($1000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 Part A: Develop an estimated regression equation with the amount of television advertising as the independent variable.  Part B: Develop an estimated regression equation with both television advertising and news paper advertising as independent variables. Part C: Is the estimated regression rquation coefficient for television advertising expenditures the same in part (a) and in part (b) ? Interpret the coefficient in each case.  Part D : Predict Weekly gross revenue for a week $3500 is spent on television advertising and $1800 is spent on newspaper advertising?  Please hurry

Bowling Corporation wants to know how closely its current cost driver is correlated with its monthly operating costs. The managerial accountant runs a regression analysis using a statistical software program and produces the following data: Intercept Coefficient = 12,200,567 X Variable 1 Coefficient = 95.65 R-square= 0.8574 What is the Bowling Corporation's monthly cost equation? OA. y $95.65x + $12,200,567 OB. y $95.65x + $8.574 O c. y = $0.8574x + $12,200,567 O D. y = $12,200,567x + $8,574 ---

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…