Interpret the following regression equation with (UR) uemployment rate as dependent variable and (INF) inflation as the independent variable.
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Interpret the following regression equation with (UR) uemployment rate as dependent variable and (INF) inflation as the independent variable.
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- On minitab or excel Calculating the coefficient of determination of a set of data, it was obtained that r^2 = 0.845 . Based on this value, it can be concluded that: Options: The least squares line provided a good fit as a large proportion of the variability in "y" has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in "y" has been explained by the least squares line. The least squares line did not provide a good fit since a small proportion of the variability in "y" has been explained by the least squares line. The least squares line did not provide a good fit since a large proportion of the variability in "y" has been explained by the least squares line.elogin ||| X 12 McGraw-Hill Campus - Intro to S.X https://www-awu.aleks.com/alekscgi/x/Isl.exe/1o_u IgNslkr7j8P3jH-IBlu6HJ1SV O DESCRIPTIVE STATISTICS Finding quartiles Median: The following are the ages of 13 physics teachers in a school district. 24, 27, 31, 33, 35, 36, 39, 41, 43, 46, 46, 55, 56 Notice that the ages are ordered from least to greatest. Give the median, lower quartile, and upper quartile for the data set. McGraw-Hill (b) Lower quartile: (c) Upper quartile: 0 Explanation Check XThe Average final score was 78, the average midterm score was 80. The standard deviation for the average final midterm score was 4.5 and the standard deviation for the average midterm score was 5.5. The correlation coefficient is 0.76 Find the least squares regression line. Supposed that wanted to find to predict the final exam score based the midterm score
- call: Researchers measured the percent 1m(formula = Symptoms - wear_mask, data - some_states) of people in 25 states who ʻknew someone with COVID symptoms' (ŷ) and regressed this on the percent of the population frequently wearing a mask in public (x). Residuals: Min -7.9167 -2.3306 -0.2469 2.5020 7. 3345 10 Median 30 Маx coefficients: (Intercept) 111.0981 wear_mask Estimate std. Error t value Pr (>|t|) 10. 5423 10. 538 2.82e-10 *** -8. 375 1. 94 e-08 *** -1.0419 0.1244 signif. codes: 0 ****' 0.001 ***' 0.01 **' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.859 on 23 degrees of freedom Multiple R-squared: 0.7531, F-statistic: 70.15 on 1 and 23 DF, p-value: 1.936e-08 Adjusted R-squared: 0.7423 If 75 percent of people in a state wear masks regularly, what % of people does this model predict will know someone with COVID symptons? 1) 32 2) 33 3) 34 4) 35a residual is the distance between the data value in the line of best fit true or false?Tire pressure (psi) and mileage (mpg) were recorded for a random sample of seven cars of thesame make and model. The extended data table (left) and fit model report (right) are based on aquadratic model What is the predicted average mileage at tire pressure x = 31?
- The standard error of the mean (Qx) is a(n) estimate of population means which make up the sampling distribution. indication of variability in the distribution of sample means indication of variability in the raw data derived from the sample measure of variability in the populationQ3 An automobile engineer claim that his team has successfully designed a new engine that saves more car fuel than the previous designs. He wishes to prove that by conducting on road experiment to compare both designs. A sample of 40 cars of previous and new engines version were involved in the experiment. The data of fuel consumption (liter/100km) were recorded in Table Q3. Table Q3 Data of fuel consumption (liter/100km) Mean Variance Previous version New version 5.9 0.017 5.4 0.022 (a) State the type of data collection involved in the given case study. Justify your answer. (b) Construct 90% confidence interval for the different mean between previous and new engine version. (c) Suppose the sampling for both engine versions were reduced to 15. Test the engineer claim at 0.05 significance level.The following results were obtained by regressing mean hourly wage in dollars (Y) on years of schooling (X). Dependent Variable: MEAN_WAGE Method: Least Squares Date: 02/15/15 Time: 11:11 Sample: 1 13 Included observations: 13 Variable Coefficient Std. Error t-Statistic Prob. C -0.014453 0.874624 -0.016525 0.9871 YEARS_SCHOOLING 0.724097 0.069581 10.40648 0.0000 R-squared 0.907791 Mean dependent var 8.674708 Adjusted R-squared 0.899409 S.D. dependent var 2.959706 S.E. of regression 0.938704 Akaike info criterion 2.852004 Sum squared resid 9.692810 Schwarz criterion 2.938920 Log likelihood -16.53803 Hannan-Quinn criter. 2.834139…
- Explain Consistency and Asymptotic Normality of the OLS Estimators?Benign prostatic hyperplasia is a noncancerous enlargement of the prostate gland that adversely affects the quality of life (QoL) of millions of men. A study of minimally invasive procedures for the treatment for this condition looked at pretreatment QoL (qol_base) and quality of life after 3 month on treatment (qol_3mo) The baseline data for 10 patients and their 3 month follow-up data is presented below: MAXFLO_B = maximum urine flow at baseline (urine flow measurement scale misplaced) MAXFLO3M = maximum urine flow after 3 months of treatment maxflo_b maxflo3m 7 8 18 8 13 9. 16 11 8 4 12 10 8 14 10 13is a scatterplot with RSquared equal to 0.022418 and Root Mean Square error equal to 2.422091 a good model and what do those two valus tell you about the graph. Picture of scatter plot attached.