Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predicta day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximumtemperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted inthe scatter plot in Figure 1.Temperature, x(in degrees Fahrenheit)Coffee sales, y(in dollars)74.42014.374.81495.873.51690.82400-56.61610.92200+47.82158.02000-40.12302.31800-67.81726.81600-58.01957.81400+83.51541.7120036.22001.047.41978,8607051.72177.3Temperature(in degrees Fahrenheit)71.81934.0Figure 161.91869.3Coffee sales(in dollars) 45.01802.2Send data to calculatorSend data to ExcelThe least-squares regression line for these data has a slope of approximately -10.64.Answer the following. Carry your intermediate computations to at least four decimal places, and round your answers as specified below. (If necessary, consult alist of formulas.)(a) What is the value of the y-intercept of the least-squares regression line for these data?Round your answer to at least two decimal places.(b) What is the value of the sample correlation coefficient for these data?Round your answer to at least three decimal places.

The given data is, Temperature(x) Coffee sales 74.4 2014.3 74.8 1495.8 73.5 1690.8 56.6…

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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From the parameter estimation output, which of the following is FALSE? Coefficients Standard Error t Stat p-value 0.783 0.0159 Intercept Diameter (mm) of granules of sand -2.476 -3.161 17.159 2.034 8.438 0.000065 For a one-unit increase in the independent variable, the predicted-y is decrease by – 2.476. The independent variable is significant. For a one-unit increase in the independent variable, the predicted-y is increase by 17.159. The predictor variable is significant.
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation?
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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
We give the total variation, the unexplained variation (SSE), and the least squares point estimate b1 . Total variation = 13.459; SSE = 2.806; b1 = 2.6652 Click here for the Excel Data File Using the information given, find the explained variation, the simple coefficient of determination (r2), and the simple correlation coefficient (r). Interpret r2. (Round your answers to 3 decimal places. Round your percent to 1 decimal place.) Explained variation r2 r % of the variation in demand can be explained by variation in price differential.
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.
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Prev The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting the number of bids an Item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, It would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 28 33 36 42 45 Number of Bids 1 7 8 9 10 Step 3 of 6: Find the estimated value of y when x = 33. Round your answer to three decimal places. Table Copy Data Next
write the regression code for the following data based on the below instruction, in STATA. hint: occupation=a categorical variable equal to ”1” if a person’s occupation is ”management”; ”2” if it is ”sales”; ”3” if it is ”clerical”, ”4” if it is ”service”, ”5” if it is ”professional” and ”6” if it is ”other”. Please make sure to use ”other” as the base category;
Prev The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density 61 62 68 69 40 357 350 343 340 315 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Table Copy Data Next

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What is the value of the y-intercept of the least-squares regression line for these data? Round your answer to at least two decimal places. What is the value of the sample correlation coefficient for these data? Round your answer to at least three decimal places.