Il the iinear correlation between two vaiables Is negative, what can be sald about the slope of the regression Tine? ..... Choose the correct answer below. O A. More information is needed B. Negative O C. Positive
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- can someone produce SPSS results by including the the intercept (a.k.a. the constant), standard error of estimate, unstandardized regression coefficient, the p-value for the unstandardized coefficient, R-square, Beta (i.e. standardized regression coefficient) using Excel with this given data? Overall: 3.4, 2.9, 2.6, 3.8, 3.0, 2.5, 3.9, 4.3, 3.8, 3.4, 2.8, 2.9, 4.1, 2.7, 3.9, 4.1, 4.2, 3.1, 4.1, 3.6, 4.3, 4.0, 2.1, 3.8, 2.7, 4.4, 3.1, 3.6, 3.9, 2.9, 3.7, 2.8, 3.3, 3.7, 4.2, 2.9, 3.9, 3.5, 3.8, 4.0, 3.1, 4.2, 2.9, 3.9, 3.5, 3.8, 4.0, 3.1, 4.2, 3.0, 4.8, 3.0, 4.4, 4.4, 3.4, 4.0, 3.5 Teach: 3.8, 2.8, 2.2, 3.5, 3.2, 2.7, 4.1, 4.2, 3.7, 3.7, 3.3, 3.3, 4.1, 3.1, 2.9, 4.5, 4.3, 3.7, 4.2, 4.0, 3.7, 4.0 2.9, 4.0, 3.3, 4.4, 3.4, 3.8, 3.7, 3.1, 3.8, 3.2, 3.5, 3.8, 4.4, 3.7, 4.00, 3.4, 3.2, 3.8, 3.7, 4.3, 3.4, 4.0 3.1, 4.5, 4.8, 3.4, 4.2, 3.4 Exam: 3.8 3.2, 1.9, 3.5, 2.8, 3.8, 3.8, 4.1, 3.6, 3.6, 3.5, 3.3, 3.6, 3.8, 3.8, 4.2, 4.1, 4.0, 4.3, 4.2, 4.0, 4.1, 2.7, 4.4, 4.4, 4.3, 3.6, 4.1, 4.2, 3.6,…A researcher collected data on people's heights and their shoe sizes from 500 people (N=500). Then, she convert the two variables (X-: heig ht, Y: shoe size) to standard scores before computing regression constants and the correlation coefficient between the two variables. The correlation coefficient (from the standardized scores) was found to be r = .37. Which of the following statements is CORRECT? a. Although the correlation coefficient is positive (r=.37), the regression slope can be negative O b. If the correlation coefficient was computed from raw scores, it will not be r=.37 O c. The regression slope (from the standardized scores) is the same as the correlation coefficient (r=.37) Od. The Y-intercept (from the standardized scores) is the same as the correlation coefficient (r=.37)state whether is true or false QUESTION 16The most commonly used measure of central tendency of a distribution of scores is the medianbecause it indicates the middle value.QUESTION 17In the simple linear regression model y D 0 C 1x C "; the error term values explain the variationin the y–values that could be observed when the independent variable is x:
- 7. Given No. of pairs = 12 %3D Y A M 74.50 125.50 Std. Deviation 13.07 15.85 Summation of products of corresponding deviations from respective means =2176. Calculate the co-efficient of correlation between X and Y and find the regressions equations. CS Scanned with CamScannerhos (Y 00 5. The following results were declared in Economics and Mathematics in B. Com examination. Scores in Economics (X) Scores in Mathematics (Y) Mean 40 30 Standard deviation 10 20 Karl Pearson's coefficient of correlation between X and Y = +0.4. Find both the regression lines. Using these regression lines, estimate the value of Y for X = 40 and estimate the value of X for Y = 20.please answer Alll
- A group of 13 health children and adolescents participated in a study designed to analyze the relationship between age (AGE in years) and average total sleep time (ATST in minutes). To obtain a measure for ATST, recordings were taken on each subject on three consecutive nights and then averaged. The results obtained are given below along with the corresponding scatter plot. The estimated regression line is Y-hat= 646.483 – 14.041x Interpret the slope and intercept of the estimated regression line. Is it meaningful?Help with the problem1. Greater than or less than? 2. Increase or decrease?
- Explain Which of the following statements are true about studentized residuals? They are the deviations of the observed values from the fitted line. They're closely related to the errors of prediction of the observation. Large studentized residuals will lead to large influence on the regression model in all circumstancesWhich of the following statements are not correct? O The coefficient of determination, denoted by r^2 is interpreted as the proportion of observed y variation that cannot be explained by the simple linear regression model. O The higher the value of the coefficient of determination, the more successful is the simple linear regression model in explaining y variation. If the coefficient of determination is small, an analyst will usually want to search for an alternative model (either a nonlinear model or a multiple regression model that involves more than a single independent variable). The coefficient of determination can assume any value between 0 and 1, inclusive.Please help asap I can’t get any more questions wrong