Historical cost principle states that

Working Hard Company offers services to individuals desiring to improve their personal images. After the accounts have been adjusted at December 31, the end of the fiscal year, the following balances were taken from the ledger of Working Hard Company:   Fees Revenue          $1,000,000 Rent Expense             $200,000 Retained Earnings     $450,000 Supplies Expense        $20,000 Wages Expense          $500,000 Miscellaneous Expense $30,000   The closing entry includes a:

Indicate whether each account would flow into the income statement, statement of stockholders’ equity, or balance sheet

Suppose that R2=[r2] and there are [p] explanatory variables and [n] observations.  Find the value for adjusted R2.  Answer to four decimal places.

Which of the following is a test for heteroskedasticity?

Use the toy data set here for this problem. a)  What is the mean of Y? [a_pt4366]. b) What is the standard deviation of T? [b_5pt76]. c) Construct a boxplot of U.  Based on the boxplot, how many outliers are there? [c_2]. d) Run a logistic regression with Y as the dependent variable and T, U and V as the explanatory variables. What is the estimated coefficient on T? [d_negpt899]. e) Construct a 90% confidence interval for the coefficient on T.  Choose the values for the interval. [e_neg1pt444]. f) At the 0.05 level of significance test the hypothesis that the coefficient on U is 1.  You  [f_failtoreject]. g) What is the concordance for your regression? [g_pt9758]. h) What is the estimated probability that Y=1 when T= 17 and U = 17 and V= 27? [h_pt9709]. i) What is the log odds for that scenario? [i_3pt509]. j) Which observation has the most “unexpected” outcome?  That is the observation where the outcome was least consistent with the probability of success assigned by the model. [j_56].    

If two explanatory variables have a correlation that exceeds 0.5 or is less than -.5, they cannot be used together in the same model.

Concerning independent variables, a model may have only the original variable or a transformation of the variable but not both.

The picture here is from a procedure in R.  If it does not load in your browser you can open the pdf file here which also contains an image of the output. Use the output to answer the following questions. a) Which procedure in R produced the output? [a_leaps]. b) Which variable does the procedure suggest is the best variable to include in your model?  That is, if you could only include one variable which variable would that be? The variables are x1, x2, x3, …. [b_x6]. c) Which variables does the procedure suggest belong in the final model? [c_all]. d) T/F: it is possible to use the results in the output to calculate the VIF. [d_False]. e) Examine the results to find the stage at which you would add x2.  Compare this model to the model that does not include x2, that is the model before adding x2.  What is the reduction in SSE/MSE from adding x2 to the model. [e_1324].  

Calculate the VIF for the explanatory variable X4.  When X4 is regressed on the other explanatory variables the resulting R squared is [r2]. Answer to 4 decimal places.