(a) A yoga instructor collected student data. She found that students who had less sleep did not tend to burn more or fewer calories during class. What can she conclude? There is no correlation between amount of sleep and number of calories burned. There is a correlation between amount of sleep and number of calories burned. There may or may not be causation. Further studies would have to be done to determine this. There is a correlation between amount of sleep and number of calories burned. There is probably also causation. This is because there is likely an increase in the number of calories burned with an increase in the amount of sleep. (b) A traffic study determined that a larger city population indicates a longer commute time for drivers. What can we say from this information? There is no correlation between commute time and city population. There is a correlation between commute time and city population. However, there is no causation. This is because there is an increase in the commute time with an increase in the city population. There is a correlation between commute time and city population. There may or may not be causation. Further studies would have to be done to determine this. (c) A company found that employees who start work earlier do not necessarily drink more or less coffee. Which statement is most likely true? There is no correlation between start time and amount of coffee consumed. There is a correlation between start time and amount of coffee consumed. There may or may not be causation. Further studies would have to be done to determine this. There is a correlation between start time and amount of coffee consumed. However, there is no causation. This is because there is probably an increase in the amount of coffee consumed with an earlier start time.

(b) A grocery store conducted a survey. It showed that when customers buy less cereal, it does not indicate a change in the amount of milk they purchase. What can we conclude? O There is no correlation between amount of cereal bought and amount of milk purchased. O There is a correlation between amount of cereal bought and amount of milk purchased. However, there is no causation. This is because there is likely a decrease in the amount of milk purchased with a decrease in the amount of cereal bought. O There is a correlation between amount of cereal bought and amount of milk purchased. There may or may not be causation. Further studies would have to be done to determine this. (c) John compared the player statistics from team's basketball season. He determined that being taller does not imply that a player scores more or fewer points. What is likely true? O There is no correlation between height and number of points scored. O There is a correlation between height and number of points…

A research study was conducted about gender differences in "sexting." The researcher believed that the proportion of girls involved in “sexting” is less than the proportion of boys involved. The data collected in the spring of 2010 among a random sample of middle and high school students in a large school district in the southern United States is summarized in Table. Is the proportion of girls sending sexts less than the proportion of boys "sexting?" Test at a 1% level of significance. Table 2: Frequency of "sexts" sent by gender Gender Males Females Sent "sexts" 183 156 Total number surveyed 2231 2169

Rondale notices that the best bowlers in his bowling league tend to be very outgoing. To test this hypothesis, he records everyone’s average bowling scores and asks them fill out a questionnaire to measure their extroversion scores. He then statistically analyzes the relationship between these 2 variables. What kind of study design did Rondale use? Group of answer choices A. Descriptive B. Experimental C. Case D. Correlational

Answer the questions below. (a) A computer manufacturer conducted a survey. It showed that a younger customer will not necessarily purchase a lower or higher priced computer. What is likely true? There is no correlation between age and purchase price. There is a correlation between age and purchase price. There may or may not be causation. Further studies would have to be done to determine this. There is a correlation between age and purchase price. There is probably also causation. This is because there is likely a decrease in the purchase price with a decrease in age. (b) A hospital found that a lower outside temperature indicates a higher number of patient visits. What can we determine from this information? There is no correlation between outside temperature and number of patient visits. There is a correlation between outside temperature and number of patient visits. However, there is no causation. This is because there is an increase in the number of patient visits with a decrease…

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In a study looking at undergraduate students' perceptions of sense of community at their university, a researcher hypothesizes that the farther away students live from campus (in miles), the less they feel they are part of the university community. The researcher collected data for the following two variables - miles from campus and part of community (continuous rating scale from 1-10 of how much they felt part of the university community 1. Run a PEARSON CORRELATION in Jamovi and report your findings in APA. Miles from campus Part of community 2 9. 19 7 32 3 23 9. 40 5 5 20 1 16 2 10 8 30 5 3 4 36 8 17 4 7 25 4 12 4

The accompanying technology output was obtained by using the paired data consisting of foot lengths​ (cm) and heights​ (cm) of a sample of 40 people. Along with the paired sample​ data, the technology was also given a foot length of 21.4 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 21.4 ​cm, what is the single value that is the best-predicted height for that​ person? The regression equation is Height=52.3+4.58 Foot Length   Predictor Coef SE Coef T P Constant 52.33 11.01 4.75 0.000 Foot Length 4.5818 0.4872 9.40 0.000   S=5.50437 ​R-Sq=70.8​% ​R-Sq(adj)=70.0​%   Predicted Values for New Observations   New Obs Fit SE Fit ​95% CI ​95% PI 1 150.381 1.722 ​(146.730​, 154.032​) ​(138.758​, 162.004​)   Values of Predictors for New Observations   New Obs Foot Length 1 21.4 The…

Answer the questions below. (a) A phone company determined that an increase in income does not indicate a change in a customer's phone usage. What does the analysis show? OThere is no correlation between income and phone usage. There is a correlation between income and phone usage. There may or may not be causation. Further studies would have to be done to determine this. OThere is a correlation between income and phone usage. There is probably also causation. This is because there is likely an increase in phone usage with an increase in income. (b) A music store conducted a survey. It showed that the number of CDs purchased by a shopper decreases with the number of songs he downloaded. What can we likely say? There is no correlation between number of CDs purchased and number of songs downloaded. There is a correlation between number of CDs purchased and number of songs downloaded. However, there is no causation. This is because there is a decrease in the number of CDs purchased with…

Read through this scenario and look at the data that was collected.  State the null and all possible research hypotheses.  Review the results below (I used SPSS) and answer the questions that follow. Scenario:  A researcher wants to see if gender and / or income affects the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help.  The independent variables are gender, income, and the interaction of gender and income.  The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affects the total amount of help offered.  Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA.  His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect.  The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables…