Recall our beloved linear regression model: y = XB+ € Where E(e|X) = 0 and Var(e|X) = Ito². The Ordinary Least Squares (OLS) estimator is given by: BOLS = (X'X)-1X'y (1) Derive it's conditional mean and variance. Analyse, to the best of your ability, the quality of this estimator.
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- A researcher wants to know if there is a significant correlation between hours spent studying for an exam (X) and exam performance (Y). Level of significance is 0.05, degrees of freedom is 3, and the critical value is 0.878. Interpret the strength and direction of the correlation, calculate and interpret the coefficient of determination, then develop the simple regression equation for the two variables. X (Time Spent Studying in Hours) Y (Exam Performance, 0 to 100) 3 56 6 77 7 79 8 70 11 96 Mean 7.00 75.60 SD 2.92 14.54 For this question, on your hand calculation document clearly state: a) the null hypothesis, b) the alternative hypothesis, c) the alpha level you are using, d) the critical value, e) process for calculating r, f) your decision about the null hypothesis, g) the coefficient of determination, h) and regression equation. This is what I will be marking. You may also enter your responses here, but it is…c) Give the expression of R˜^2 in terms of R^2 , and justify your answer.3
- Where we observe multicollinearity in a multiple regression analysis, two of our independent variables, x-1 and x-4, are so highly correlated they’re almost indistinguishable with respect to their relationship with our dependent (y) variable. Why is that a problem?Suppose you are to estimate a simple regression for the following population model: Y=B₁ + B₁X + µl From a population of over thousands of observations, a small number of samples were randomly selected. The following is some of the information from the randomly selected sample.A study was conducted to see whether heart rate (y) on swimmers linearly related to their age (x1) and swimming time for 2000 meters (x2). A random sample of ten swimmers was selected and the result is shown in the following Microsoft Excel output. (a)Interpret the value of R2 from the output. (b)Conduct a hypothesis test to test whether the linear regression model is fit or not using a = 0.05. (c)Calculate the 95% confidence interval for the coefficient value for age.
- Some non-linear regressions can also be estimated using a linear regression model (using 'linearization'). Assume that the data below show the selling prices y (in dollars) of a certain equipment against its age x (in years). We'd like to fit a non-linear regression in the form y = cd* to estimate parameters c and d from the data by linearizing the model through In y In c+ (In d)x = b, + b, x. y y 6381 3 5394 5673 4980 2 5740 4896 (Click the button to copy or download the data.) Using Excel ot other software, the non-linear regression model y = cd can be estimated as: y = D*. (Round c and d to four decimal places, inlcuding any zeros.)Find the least square regression line."given a simple regression with slope b=3, s (sub y)=8, and s (sub x)= 2, and n=30. Find the standard error of the estimate."
- The US government is interested in understanding what predicts death rates. They have a set of data that includes the number of deaths in each state, the number of deaths resulting from vehicle accidents (VEHICLE), the number of people dying from diabetes (DIABETES), the number of deaths related to the flu (FLU) and the number of homicide deaths (HOMICIDE). Your run a regression to predict deaths and get the following output: At α = .10, which variable(s) is/are significant predictors of deaths?A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is: