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- 1HOME Realty claims that it can sell a detached, residential house faster than any other realty company. With the aim of examining HOME's claim, you sample 20 customers who sold a detached, residential house through HOME and record the selling times (in days) of the houses. Your data are summarized in the following histogram. Relative frequency 0.4- 0.3 0.35 0.20 0.2+ 0.20 0.15 0.10 0.1- 0.0- 0 10 20 30 Selling time (in days) 40 50 Based on the histogram, find the proportion of selling times in the sample that are at least 20 days. Write your answer as a decimal, and do not round your answer. 0 × 5For the table below. Determine the regression coefficient (with confidence interval), and the adjustment line:
- Use the table below to answer the following questions: (a) Find the value of the linear correlation coefficient. (b) Assuming a 0.01 significance level, find the critical values. (c) Is there sufficient evidence to support the claim of a linear correlation? Explain your reasoningConstruct the scatter plot for the following data. From the plot does there appear to be a linear correlation? Data x 2 -4 -2 -1 -2 -1 -4 6 -5 -2 y -1 -7 2 -7 4 -2 -5 4 -4 -5 2. Find the value of the linear correlation coefficient. 3. Perform a hypothesis test on the claim that there is a linear correlation. Does there appear to be a linear correlation? Why or why not? (Use the traditional method) List the null and alternative hypothesis, the critical value, the test statistic, Draw the graph and label the test statistic, critical value and rejection regions. Reject or fail to reject? State the conclusionThe following data represents the winning percentage (the number of wins out of 162 games in a season) as well as the teams Earned Run Average, or ERA.The ERA is a pitching statistic. The lower the ERA, the less runs an opponent will score per game. Smaller ERA's reflect (i) a good pitching staff and (ii) a good team defense. You are to investigate the relationship between a team's winning percentage - Y, and its Earned Run Average (ERA) - X. Winning Proportion - Y Earned Run Average (ERA) - X 0.623457 3.13 0.512346 3.97 0.635802 3.68 0.604938 3.92 0.518519 4.00 0.580247 4.12 0.413580 4.29 0.407407 4.62 0.462963 3.89 0.450617 5.20 0.487654 4.36 0.456790 4.91 0.574047 3.75 (b) Use R-Studio to find the least squares estimate of the linear model that expressed a teams winning percentage as a linear function of is ERA. Use four decimals in each of your answers.Y^i = ? + - Xi(e) A certain professional baseball team had an earned run average of 3.45 this past…
- The table below provides a statistical summary of the number of CDs owned as reported by students in a class survey done at Penn State University. Variable N Mean Minimum Q1 Median Q3 Maximum CDs 250 85 0 30 50 100 500 Approximately what percent of students own somewhere between 30 and 50 CDs? MacBook Pro O 50% O 4% O 20% O 25% tv COUNGOWIT to Disney ang ikala Cistyear! #3 E D $ 4 R Q Search or enter address % A 6 5 T L C Y 87 & + U * ∞ 8 ←1 J ( 9 ll 0 0Q9 The explanatory variable in linear correlation/regression model is recorded on which of thesemeasurement scales: A. categorical B. ordinal C. quantitative D. none of the aboveYou wish to determine if there is a linear correlation between the two variables at a significance level of α=0.05α=0.05. You have the following bivariate data set. x y 43.5 181.7 81.9 106.1 74.4 58.4 99 1.8 83.1 -6.3 29.5 150.5 62.6 44.7 66.4 54 60.5 142.9 66.7 48.7 60.3 41 61.7 171.8 What is the critival value for this hypothesis test?rc.v. = What is the correlation coefficient for this data set?r =
- There is a strong relationship between heterosexual couples attractiveness scores. Therefore, a researcher wanted to determine if husbands attractiveness score may be predicted from their counterparts’ scores. Below are couples attractiveness ratings. Observation Wife (X) Husband (Y) 1 11 14 2 6 7 3 16 15 4 4 7 5 1 3 6 10 9 7 5 9 8 3 8 Complete the following ANOVA table Source SS DF MS F SSreg SSres SStotalIt is assumed that standardized test scores should be correlated with students' classroom performance. One would expect that students who consistently perform well in the classroom (on tests, quizzes, etc.) would also perform well on a standardized test scored from 0-100 (with 100 indicating high achievement). A teacher decides to examine this hypothesis. At the end of the academic year, she collects the following data for her (admittedly small) class: Standardized test score Overall GPA 56 1.8 1 73 2.6 2 78 2.9 3 83 2.9 4 85 3.0 5 rows x 2 columns What is your best guess for the expected GPA of a student who scored a 88 on the standardized test? number (rtol=0.01, atol=0.0001) Determine a 95% confidence interval for the expected GPA of a student who scored a 76 on the standardized test. Ib number (rtol=0.01, atol=0.0001) ub number (rtol=0.01, atol=0.0001)You wish to determine if there is a linear correlation between the two variables at a significance level of α=0.02α=0.02. You have the following bivariate data set. x y 25.2 64.3 15.3 79.6 39 49.8 30 75.1 14.8 82.2 15.7 69.9 4.3 92.5 17.9 84.4 13.4 77.6 18.4 74.4 25 78.1 24.4 65 23.7 69.7 10.9 89.1 31.3 65.3 20.6 82.4 20.4 81.1 29 68.2 13.9 92 8.7 88.4 5.3 93.4 What is the correlation coefficient for this data set? r = (report answer accurate to at least 3 decimal places)What is the p-value for this correlation coefficient? p-value =