The accompanying table shows the number of cars sold last month by seven employees at a car dealership and their number of years of sales experience. Management would like to use simple regression analysis to estimate monthly car sales using the number of years of sales experience. What conclusion can be drawn from the 95% confidence interval for the slope? O Click the icon to view the car sales data. OA Because the confidence interval does not indlude zero, there is supporting evidence to conclude that a linear relationship does exist between number of cars sold and number of years of sales experience. OB. Because the confidence interval includes zero, there is supporting evidence that no linear relationship exists between number of cars sold and number of years of sales experience. OC. The dealership can be 95% confidence that for each year of sales experience a salenperson has acquired, monthly sales will increase by 1.7 to 122 cars. OD. The dealership can be 95% confident that the number of cars sold willincrease by 66 for ea ch year of sales erperience. Sales data Experience Sales 14 13 10 Click to select your answer y6-o

The accompanying table shows the number of cars sold last month by seven employees at a car dealership and their number of years of sales experience. Management would like to use simple regression analysis to estimate monthly car sales using the number of years of sales experience. What conclusion can be drawn from the 95% confidence interval for the slope? O Click the icon to view the car sales data. OA Because the confidence interval does not indlude zero, there is supporting evidence to conclude that a linear relationship does exist between number of cars sold and number of years of sales experience. OB. Because the confidence interval includes zero, there is supporting evidence that no linear relationship exists between number of cars sold and number of years of sales experience. OC. The dealership can be 95% confidence that for each year of sales experience a salenperson has acquired, monthly sales will increase by 1.7 to 122 cars. OD. The dealership can be 95% confident that the number of cars sold willincrease by 66 for ea ch year of sales erperience. Sales data Experience Sales 14 13 10 Click to select your answer y6-o

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

A linear model was fit to predict weekly Sales of frozen pizza (in pounds) from the average Price ($/unit) charged by a sample of stores in a city in 39 weeks over a three -year period. The average Sales were 63, 200 pounds (SD = 10, 670 pounds), and the correlation between Price and Sales was -0.529. If the Price in a particular week was two SD higher than the mean Price, how much pizza would you predict was sold that week?
A rural state wants to encourage high school graduates to continue their education and attend college. The state collected information on a random sample of high school seniors from across the state 7 years ago and is now observing how many years of education they completed. They believe students decide to achieve more education when they are more capable, have easier access to college education, and the opportunity cost of attending are lower. To explore the factors that affect the years of education completed they have used multiple regression to estimate the years of completed education as a function of: Unemployment rate - the unemployment rate in the county (3.9 – 16.8) County Hr Wage - average starting hourly manufacturing wage in the county Test - student score on college admission test (0 to 100 scale) Dist to college - Distance to near college (measured in 100’s of miles) Tuition - Tuition charged at nearest state university (measured in $1000s)…
The table below lists weights (carats) and prices (dollars) of randomly selected diamonds. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence o 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 a diamond that weighs 0.8 carats. Weight Price 0.3 $508 a. Find the explained variation. 0.4 $1153 0.5 $1332 Round to the nearest whole number as needed.) . Find the unexplained variation. Round to the nearest whole number as needed.) c. Find the indicated prediction interval.
Please answer... I'm needed only 1 hours time to dd .... Thank u
You plan to fit a regression model that will be used to predict first-year college GPA (FYGPA) from high-school GPA (HSGPA), ACT score (ACT), first-generation status (Yes or No), socioeconomic class (lower class, lower to middle class, middle to upper class, and upper class), and school type (public or private). What is the total number of estimated regression coefficients? If the sample size is n = 250 students, what are the degrees of freedom for the following sources of variation: Regression Error Total
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.
Use Excel’s Data Analysis tool to conduct a multiple regression, using Risk of Stroke as the dependent variable, and all the rest of variables as independent variables. Choose confidence level at 99%. Risk of Stroke (%) Age Pressure Smoker (Yes=1) 12 57 152 0 24 67 163 0 13 58 155 0 56 86 177 1 28 59 196 0 51 76 189 0 18 56 155 1 31 78 120 0 37 80 135 1 15 78 98 0 22 71 152 0 36 70 173 1 15 67 135 1 48 77 209 0 15 60 199 0 36 82 119 1 8 66 166 0 34 80 125 1 3 62 117 0 37 59 207 1 Interpret the multiple coefficient of determination R-square. Compare the R-square of the simple linear regression and the R-square of the multiple regression, what does the differences in R-squares tell us? What does F-test for significance tell us?
The full question for the last question is written down below Fail/fail to reject H0. There is insufficient/sufficient evidence at the 10% level of significance to support the claim that there is a linear relationship between weight and number of hours slept.
A small independent organic food store offers a variety of specialty coffees. To determine whether price has an impact on sales, the managers kept track of how many kilograms of each variety of coffee were sold last month. The data and summary statistics (including the averages, standard deviations and the correlation coefficient) are shown in the table. If the "price per kilogram" is the predictor variable and "kilograms sold" is the response variable, then use a regression line to predict the the amount of kilograms sold for a "price per kilogram" equal to $15. Round your numbers to 3 decimal places.
Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
Hawk thinks his heartrate will increase as he increases his skateboarding speed. To see if this relationship exists, he records six different speeds and models it with a scatterplot and regression output. Part A: Write the equation of the regression line using the regression output. Part B: What do the slope and intercept parameters mean using the context of the problem? Part C: Compute the margin of error that Hawk should use if he wants to provide a 99% confidence interval for the slope. Assume that conditions for inference are satisfied.