The store manager of a local supermarket is interested in determining the relationship between the amount of money spent by customers and the size of their household. Using monthly data from all months in 2018, the following model was estimated: Y = 0.01 + 38.61 X                   Error sum of squares: 521.6 Sum of squares of X: 2323.3   Determine the lower limit of the 99% confidence interval for the slope correct to two decimal places.

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…

Researchers are examining the relationship between hours of sleep and athletic performance among college athletes. Athletic performance will be measured on a numeric scale, with greater numbers indicating better performance. The researchers expect that the more hours the athletes sleep, the better they will perform. Assuming all conditions for inference are met, the researchers will create a 95 percent confidence interval for the slope of the regression line for predicting athletic performance from amount of sleep. For which of the following would the confidence interval support the researchers' expectations? (A) The confidence interval includes only positive values. (B) The confidence interval includes only negative values. (C) The confidence interval has a width less than 1. (D) The confidence interval has a width greater than 1. (E) The confidence interval includes the value 0.

Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude Temperature a. Find the explained variation. (Round to two decimal places as needed.) 2 55 8 40 13 25 20 - 3 28 - 26 31 - 41 34 - 53

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In this question, we investigate the relationship between the top (maximum) speed (mph) and maximum height for a random sample of roller coasters in the United States. Here is the scatterplot and a summary of the simple linear regression model.   Construct a 95% confidence interval for the true slope of the regression line and interpret your confidence interval in context.

The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time​ (measured in​ hours) it takes to record a loan application. Data are collected from a sample of 30​ days, and the number of applications recorded and completion time in hours is recorded. Attached below is the regression output. What can be said about the​ 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan​ application?           Question content area bottom Part 1   A. The​ 90% confidence interval is narrower than​ [0.0109, 0.0143].   B. The​ 90% confidence interval is wider than​ [0.1492, 0.6555].   C. The​ 90% confidence interval is wider than​ [0.0109, 0.0143].

The following regression is fitted using variables identified that could be related to student requestedloan amount, LOAN-AMT($) for returning students of a certain University. ????−??? = ? + ? ?????? + ? ???? + ? ????Where ACCEPT = the percentage of applicants that was accepted by the university, PREV = previous loan amount and OUTS =outstanding loan amountThe data was processed using MNITAB and the following is an extract of the output obtained:EXHIBIT 2Predictor Coef StDev T PConstant -26780 6115 * 0.000ACCEPT 116.00 37.17 3.14 0.003PREV -4.21 14.12 -0.30 **OUTS 70.85 15.77 4.49 0.000S = 2685 R-Sq = 69.6% R-Sq(adj) = 67.7%Analysis of VarianceSource DF SS MS F PRegression 3 808139371 269379790 37.37 0.00Residual Error 49 353193051 7208021Total 52 1161332421a)What is dependent and independent variables? b)Fully write out the regression equation c)What is the sample size used in this investigation? d)Fill in the blanks identified by ‘*’ and ‘**’. e)Is β significant, at the 5% level of…

The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 + 4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.

A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the​employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees.   a. Predict the sales next month for an employee with 2.5 years of experience.   The predicted sales is 8.6 cars.   b. Compute the coefficient of determination and interpret its meaning.   The coefficient of determination is 0.234 ​ ​Therefore, about _________% of the variation in monthly sales is explained by the years of sales experience.   ​(Type an integer or decimal rounded to one decimal place as​ needed.)