Using the variable "Accounting Literacy," provide a five different title for a correlational study. Consider including further variable that will help the title to be a correlational study. Ensure that only college students-specifically, accounting students-were required to participate as respondents in the study.
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- determine the level of measurement (nominal, ordinal, interval, ratio) for each of the variables listed, and briefly state how you were able to identify the level of measurement being used. Please note: each level of measurement may be used more than once, or not at all. Student’s gender identity (male, female, other)Interpret the results of the ratios and explain what does that mean to the performance of the organization. The answer you provided should be supported by relevant literature review including scientific journal, website and related books.The Human Resources Manager for Optilytics LLC is evaluating applications for the position of Senior Data Scientist. The file OptilyticsLLC presents summary data of the applicants for the position. a. Use a PivotTable in Excel to create a joint probability table showing the probabilities associated with a randomly selected applicants sex and highest degree achieved. Use this joint probability table to answer the questions below. b. What are the marginal probabilities? What do they tell you about the probabilities associated with the sex of applicants and highest degree completed by applicants? c. If the applicant is female, what is the probability that the highest degree completed by the applicant is a PhD? d. If the highest degree completed by the applicant is a bachelors degree, what is the probability that the applicant is male? e. What is the probability that a randomly selected applicant will be a male whose highest completed degree is a PhD?
- Which of the following returns is best described as a guess and is based on an individual's expected outcomes and probability of those outcomes? This was also described as your "guess at the beginning of the semester". Realized Expected Required PromsiedA two-sample test for means was conducted to determine if the completion time for continuing education programs for nurses differed when costs are paid by employers (Yes) versus when individuals paid out of their own funds (No). The Excel Data Analysis tool results are provided. Complete parts a through c below Click the icon to view the Excel Data Analysis tool results. a. Explain how to use this information to draw a conclusion if the null hypothesis is Hoy - 50. Clearly state the correct cribcal value and p-value and your conclusion. The alternative hypothesis is H₁: The p-value is (Type an integer or a decimal rounded to Bree decimal places as needed.) Therefore, the null hypothesis. The data provide sufficient evidence to conclude that the mean completion time is Excel Data Analysis tool results t-Test Two-Sample Assuming Unequal VananDES Mean Variance Yes 34.081 445.045 No 28.601 348.065 for nurses when costs are paid by employers. Observations 12 8 Hypothesized Mean Difference 0…A study on the effect of paternal involvement on academic performance. Descriptive research Correlational research Experimental Research Historical quantitative research O Causal research
- What does it mean to use a performance report to evaluate the results achieved during a period?hello,please when I want to do a credit scoring based on the ESG factors which excell informations should I look for to do the analysis?give three academic skills that student developed being in HE (Higher Education). Reflect on learning experience and use Gibbs’ model of reflection.
- Consider again the example introduced in Section 7.5 of a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3). Figure 7.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? d. Recall the multiple regression in Figure 7.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts (a), (b), and (c)? What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? e. Add the coefficients of determination for the simple linear regression in parts (a), (b), and (c), and compare the result to the coefficient of determination for the multiple regression model in Figure 7.23. What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? f. Add age, a dummy variable for sex, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 7.23. Code the dummy variable for sex as 1 if the customer is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?Would it be beneficial to attempt to measure each office’s individual performance with respect to the scorecard measures? Why or why notSuppose you're given a data set that classifies each sample unit into one of four categories: A, B, C, the data as A = 1, B=2, C = 3, and D=4. Are the data consisting of the classifications A, B, C, and D or quantitative? Are the data consisting of the classifications A, B, C, and D qualitiative or quantitative? OA. Qualitative, because they are measured on a naturally occuring numerical scale. B. Quantitative, because they are measured on a naturally occuring numerical scale. C. Quantitative, because they can only be classified into categories. D. Qualitative, because they can only be classified into categories. *** After the data are input as 1, 2, 3, or 4, are they qualitative or quantitative? OA. Qualitative, because they cannot be meaningfully added, subtracted, multiplied, or divided. B. Qualitative, because they are measured on a naturally occurring numerical scale. OC. Quantitative, because they are measured on a naturally occurring numerical scale. OD. Quantitative, because…