Part D only please and solve without using excel only the Normal Curve table
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Given that:
For eastern DC, the mean and standard deviation are:
For western DC, the mean and standard deviation are:
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- A least squares regression model performed to predict the selling price of houses found the following equation: Pricê = 169.328+35.3Area + 0.718Lotsize - 6543Age where Price is in dollars, Area is in square feet, Lotsize is in square feet, and Age is in years. The R2 is 0.92. One of the following interpretations is correct. Which is it? Explain why. a.) Each year a house Ages, it is worth $6543 less. b.) Every extra square foot of Area is associated with an additional $35.30 in average price, for houses with a given Lotsize and Age. c.) Every dollar in price means Lotsize increases 0.718 square feet. d.) This model fits 92% of the data points exactly.I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here beforehow to solve?
- plz solve question (c) with explanation within 30-40 mins and get upvotesPlease help it’s not gradedFind & interpret the y-intercept (a) if an interpretation is possible. If it’s not possible, just report the y-intercept and explain why it can not be interpreted practically Use your model to estimate the price of a car that has 100,000 miles. Is this prediction reasonable? Why or why not? Provide a brief conclusion or discussion of the results that includes one of the following: potential problems or sources of bias are the results what you expected? are there other variables that may also be useful to predict the price of a car
- Pls help with below homework-) Show transformation of switching equation into a regression equation.I’m taking a probability and statistics class please get this correct because I’ve gotten wrong answers beforeYou performed a regression analysis of a set of bibariate data (two variables) and found that the coefficient od determination was .99 for a quadratic model and .88 for cubic model Explain which model you would use and why
- Give examples of where the use of regression analysis can be benificially be made.Using your dataset, run a regression of Y=GPA and X=# Friends.(do not need your actual data, just the regression results)a) State what this regression is attempting to analyze. “By running this regression, we areattempting to show.....”b) Write out the regression equation and describe what it shows (if Friends increase by 1, then. . . ).c) Find your hypothesized GPA when the # friends equals 17.d) Is the slope of # of Friends significantly different from zero?Include Ho, Ha, decision rule, t statistic from table, tc, decision, and conclusion.e) Is the r-squared of # of Friends significantly different from zero?Include Ho, Ha, decision rule, F statistic from table, Fc, decision, and conclusion.Write the linear model to test the hypothesis that there is no treatment effect. Clearly describe each term in the model, and the range of the subscripts. Write the null hypothesis that you are testing. Call: lm(formula = score ~ list, data = hearing) Residuals: Min 1Q Median 3Q Max -14.7500 -5.5833 -0.2083 6.3333 16.4167 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 32.750 1.612 20.315 < 2e-16 *** listList2 -3.083 2.280 -1.352 0.17955 listList3 -7.500 2.280 -3.290 0.00142 ** listList4 -7.167 2.280 -3.144 0.00225 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.898 on 92 degrees of freedom Multiple R-squared: 0.1382, Adjusted R-squared: 0.1101 F-statistic: 4.919 on 3 and 92 DF, p-value: 0.00325