The independent variable is what measures to see if you have difference between two parameters of two matched samples

aximize utility. c). The use of scarce resources to satisfy our wants. d). The use of fiscal and monetary policies to achieve economic outcomes. Flag question: Question 2 Question 21 pts 2. In a capitalist economy a). The government plays the dominant role. b). Market forces coordinate most production activities. c). The prices of most goods and services are set by the government. d). All of the above are correct. Flag question: Question 3 Question 31 pts 3. In a capitalist economy a). Most producers are driven by the desire to make profit. b). The government regulates most economic activities. c). Foreigners play no role in the economy. d). The output of most goods are planned. Flag question: Question 4 Question 41 pts 4. In drawing the graph of two variables that are related a). The Y variable goes on the horizontal axis. b). The X variable goes on the vertical axis. c). The independent variable goes on the vertical axis. d). In most cases the dependent variable goes on the vertical axis.

set of observations x1, x2,...,xn, with mean ¯x, then: Xn i=1 (xi x¯) > 0. ii. For two independent events A and B such that P(A) > 0 and P(B) > 0, then: P(A [ B) < P(A) + P(B). iii. For a random variable X, E(X2) can be less than (E(X))2. iv. Rejecting a true null hypothesis is known as the power of a test. v. A 4-by-2 contingency table which results in a 2 test statistic value of 6.724 is statistically significant at the 5% significance level.

y predictive value and correctly classifies 98% of overall cases. However, when I am looking at the "Variables in the Equation" table to see which variable is significant in predicting the model, it says that none of the variables are signficant, and gives me very strange odd ratios of each independent variable. I need help in understanding this.

nt variable in Part 1 : Part II: Part III:The dependent variable in all parts was : The constants in Part I: Part II: Part III: Bonus: Why did you not need a control in this experiment?

ial earnings of a student who works the whole week of the festivl if working partial days is not permitted 1. Identify the independent (x) variable within the contex of the situation

identified a rise in contacting STIs (sexually transmitted infections) among young adults and are interested in understanding why. The researchers measured several variables related to condom use including: likelihood of using condoms in the future, previous use of condoms, perceived ability for self-control in sexual relationships, and perceived risk for contracting STIs. Method This study is looking to understand why there has been a rise in sexually transmitted infections. Participants were asked to answer 4 different questions and the results were analyzed. Participants: Participants include 100 men and women ages ranging from 18-38. The mean age in this experiment is 24 years old and there is an equal amount of men and women. Name of Independent Variable(s): Provide a description of all independent (or grouping) variables, including their levels. Each variable should be described separately and the name of each new variable should be indented and underlined. Name of Dependent Variable: Provide a description of the dependent variable, including how it was operationalized. Following the description of all your variables you should clearly state (in a sentence) the research question you intend to investigate. Next, you should say something along the lines of, “This research has the following hypotheses: Ha: State the alternate hypothesis in BOTH conceptual and statistical form. H0: State the null hypothesis in BOTH conceptual and statistical form Also I need to know what analysis to complete in spss

there was no Age and/or Sex entered) has been created. It is called Newmarket_Cleaned.csv. Data file: Newmarket_Cleaned.csv (Links to an external site.) . Direct HTTP link: https://unh.box.com/shared/static/p8x4xlbean3rlslmfskfu74fe89yjui8.csv Please use it for this problem. Consider the “Model 4” developed in class, which contained Age, Age^2, and Sex as independent variables. First re-create this model with the cleaned dataset (you will need to create the Age^2 column; you can verify your model summary against the one from class), and then proceed with this problem. Make the necessary changes to the model to incorporate Year as a categorical explanatory variable (you may need to change how R interprets the variable, before running this model). Does the year of the race seem to be related to mean finishing time, after taking into account the other variables in Model 4? Use the 0.05 significance level. Explain your work/logic, and key output to support your answer. Using the updated model, after accounting for age and sex, what is the expected difference in finishing times for runners from 2008 versus runners from 2004? Using the updated model, after accounting for age and sex, what is the expected difference in finishing times for runners from 2014 versus 2011? In the above, the directions are to treat Year as a categorical variable. Explain how the regression model would be different if it instead were treated as a numeric variable. What are the assumptions being made about the effect of Year in the two different approaches?