What happens if the dependent variables are not correlated with each other? Will you still be able to perform MANOVA/MANCOVA?
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- The least-squares regression line relating two statistical variables is given as = 24 + 5x. Compute the residual if the actual (observed) value for y is 38 when x is 2. 4 38 2A researcher collected a data set for a random sample of 930 individuals living in and around London, with data collected over 1-year period. The Table below reports the OLS coefficient estimates (intercept not reported) and standard errors (in parentheses), where the dependent variable is [100xIn(well-being)]. Commuting time/60 -0.267 (0.039) -0.14 (0.040) Age Age squared/100 0.12 (0.040) Hours worked -0.0053 (0.001) log real income 0.0267 (0.009) Married or cohabiting 0.589 (0.032) Num. of children. -0.051 (0.015) Saves Degree 0.299 (0.022) -0.022 (0.035) The explanatory variables are: Commuting time = Number of minutes of commuting time per day; Age= Age in years; Hours worked = Hours worked per week; Log of real household income = 100xLn(real household income measured in £10,000s); Num. of children = Number of children under the age of 18; Save regularly = 1 if save regularly, 0 otherwise; University degree = 1 if has a University degree, 0 otherwise. Calculate the test statistics…The line of best fit through a set of data isy=46.539+4.023xy=46.539+4.023xAccording to this equation, what is the predicted value of the dependent variable when the independent variable has value 40?y = Round to 1 decimal place.
- A random sample of 19 companies from the Forbes 500 list was selected, and the relationship between sales, in hundreds of thousands of dollars, and profits, in hundreds of thousands of dollars, was investigated by regression. The simple linear regression model displayed was used: profits = a + B (sales), where the deviations were assumed to be independent and Normally distributed, with mean 0 and standard deviation o. This model was fit to the data using the method of least squares. The results displayed were obtained from statistical software. 2 = 0.662 S = 466.2 Parameter Std. err. of Parameter estimate parameter est. -176.644 61.16 0.092498 0.0075 Suppose the researchers test the hypotheses Ho: P = 0, II, : A > 0. The P-value of the test is: less than 0.01. between 0.05 and 0.01. O between 0.10 and 0.05. greater than 0.10. hpThe coefficients in a distributed lag regression of Y on X and its lags can be interpreted as the dynamic causal effects when the time path of X is determined randomly and independently of other factors that influence Y. Explain How?Please answer as many as your allowed too. Thank you :) A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.68b=0.168 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -1.417 1.417 0.702 -0.702 (c) If a country increases its life expectancy, the happiness index will increase decrease (d) If the life expectancy is increased by 0.5 years in a certain country, how much will the happiness index change? Round to two decimal places.(e) Use the regression line to predict the happiness index of a country with a life expectancy of 69 years. Round to two decimal places.
- The equations of the regression line between two variables are expressed as 2x-3y=0 and 4y-5x-7=0 a) identify which of two can be called regression line of Y on X and X on Y b) find the correlation coefficient c) find mean value of X and mean value of YResearchers were interested in assessing whether stress levels at the beginning of the semester are related to (i.e., correlated with) stress levels during finals week. To test this, stress was measured in 5 students at the start of the semester and then again at the end of the semester during finals week. Participant Stress 1 Stress 2 (X - MX) (Y - MY) (X - MX) (Y - MY) (X - MX)2 (Y - MY)2 1 22 22 -4.4 -6 2 32 34 5.6 6 3 24 25 -2.4 -3 4 28 30 1.6 2 5 26 29 -0.4 1 What is the observed correlation coefficient (i.e., r value)?A financial analyst is examinıng the Pela each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least-squares regression line to be y = -0.147+0.043x. This line, along with a scatter plot of her data, is shown below. Earnings per Current stock price, y (in dollars) share, x (in dollars) 36.55 1.64 14.18 0.57 41.79 1.37 39.16 1.10 2.5+ 57.70 2.71 26.95 0.90 32.65 1.70 41.94 1.17 52.79 2.56 42.72 2.01 16.89 0.76 22.46 0.58 Earnings per share, x (in dollars) 58.88 2.19 30.13 1.48 50.08 1.73 28.92 0.81 Submit Assi Continue D 2021 McGraw-H Education. All Rights Reserved. Terms of Use Privacy e to search 近 Current stock price, y (in dollars)
- An "extraneous variable" is not a problem unless it with the independent variable O is identical systematically co-varies is not correlated Wait! An "extraneous variable" is by definition never a problem, that's what "extraneous" means.7The line of best fit through a set of data isy=21.974+1.021xy=21.974+1.021xAccording to this equation, what is the predicted value of the dependent variable when the independent variable has value 140? Y=
