conditions the ols estimate of bi is unbiased
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conditions the ols estimate of bi is unbiased
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- A local teacher wants to see if teaching writing in different ways impacts student learning. She has two classes of similar characteristics. In one class, she will use Teaching Strategy A and in the other class she will use Teaching Strategy B. She collects student test scores and is ready to run her analysis in Microsoft Excel. Which test should she choose from the menu? Descriptive Statistics t-Test: Paired Two Sample for Means t-Test: Two-Sample Assuming Equal Variance Fourier AnalysisCompute the least-squares regression equation for the given data set. Use a Ti-84 caloulator. Round the slope and at least four decimal places. 4 6 12 -9 -3 5 -11 3. 31 36 3 -2 -14 Send data to Excel Regression line equation: y =I3 - x and 23 ; 88 76 ; 305 27 ; 105 64 ; 240 12 ; 50 51 ; 205 33 ; 120 50 ; 104 When regression analysis is performed according to the given x and y values (y = ax + b), which one is correlation coefficient A) 0.9291 B) hiçbiri/none of them C) -0,997 D) -0,6790 E) 0,2421
- determine the following statement is a valid reason for using nonparametric methods? - We are reluctant to assume strong conditions on the data. (True or False)Regression methods were used to analyze the data from a study investigating the relationship between roadway surfaee temperature (X) and pavement deflecetion (y). Temperature Deflection 70 0.621 77 0.657 72.1 0.64 72.8 0.623 78.3 0.661 74.5 0.641 74 0.637 72.4 0.630 75.2 0.644 76 0.639 72.7 0.637 67.8 0.627 76,6 0.652 73.4 0.63 70.5 0.627 72.1 0.631 71.2 0.641 73 0.631 72.7 0.634 17.4 0.638Thirty data points on Y and X are employed to estimate the parameters in the linear relation Y = a + bx. The computer output from the regression analysis is DEPENDENT VARIABLE: R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 30 0.5300 13.79 0.0009 VARIABLE PARAMETER STANDARD T-RATIO P-VALUE ESTIMATE ERROR INTERCEPT 93.54 46.210 2.02 0.0526 |-3.25 0.875 |-3.71 0.0009 At the 1% level of significance, the critical t-value for the test is (Write the numbers as they are, do not round them)
- From the blank, choose the option between < , > , <= , >= , = and ≠The amount of gas used by a typical household in a week (Q, in litres) is found to have a strongcorrelation with the price of gas (P, in $). For 6 observations, the following data was collected:Q P11.4 4.0014 3.5016 3.0018 2.5020 2.00(a) Create a regression relationship for this data(b) Perform a hypothesis test to determine whether the slope coefficient obtained in part (a) above is statistically significant(c) Use the regression equation in part (a) above to predict the quantity of gas used by a household when the price is $3.75 per litre(d) Calculate the price elasticity of demand for gas at a price of $3.75 (e) Using a further calculation, discuss how well the regression equation in part (a) above fits the dataList two unbiased estimators and their corresponding parameters. (Select all that apply.) p is an unbiased estimator for p̂σ is an unbiased estimator for σxp̂ is an unbiased estimator for px is an unbiased estimator for μσx is an unbiased estimator for σμ is an unbiased estimator for x
- 1. Test whether the following expression is a valid CDF. The range of validity of the variable is 0 to ∞. If it is a valid CDF, obtain the corresponding pdf and its mean. If it is not a valid CDF, test whether it is a valid pdf? If it is a valid pdf, obtain the corresponding CDF and the mean. 2x x² A(x)-[1-exp(-) ++ [¹-exp(-²3)] -+-)] = exp 5 16 x² 8(b.) Consider the fictitious set of data shown below, where the line through the data is the fitted simple linear regression line. Sketch a residual plot (It doesn't need to be perfect) on the right side of this graph. What type of transformation is needed to get a proper SLR model? Write the general form of this new SLR model. §Let sale denote the sale price for a house, sqft its square footage, and days the number of days that it has been on the market. Consider running two different regressions: First regression: sale = B1 + B2sqft + ß3days + e Second regression: sale = Bi + B2sqft + e Which of the following statements is false? In the options below, the quantities SST, SSE, and SSR are as defined when we discussed R?, that is SST = E(Y, – Ỹ)², SSE = E(Y; - Ý )², and SSR = i=1 O a. The SST from the first regression can be strictly smaller than the SST from the second regression. O b. The SSE from the first regression can be strictly smaller than the SSE from the second regression. O c. The R- from the first regression can be strictly larger than the R2 from the second regression. O d. The R2 from the second regression is equal to the R2 from the regression sqft = Yi + Y2sale + e O e. The SSR from the first regression can be strictly larger than the SSR from the second regression.