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Question 1

Most people don’t succeed in losing weight by dieting, and on average people don’t lose any weight. Proponents of the quantized-self movement believe that by tracking eating behavior, using apps like MyFitnessPal, people will lose more weight. Pedro randomly selected 25 people from his university who were interested in dieting. He then had them use MyFitnessPal to track their eating behavior. He wants to understand whether using the app leads to greater weight loss.

1a) (Must answer all correct for points on question 1a.)

What is Pedro’s research question?

What is the predictor (independent variable)?

What is the outcome (dependent variable)?

What kind of dependent (outcome) measure is this?

1b) What should his null and alternate hypotheses be in words and in symbols?

1c) Would he be more likely to conclude that there is a significant effect is he uses a one -tailed or two-tailed test?

1d) Pedro found that after 6 weeks his sample of 25 had lost an average of 2 pounds. He set an alpha level of 0.10 at the start of the study and his p-value from the analysis is 0.12.

Are Pedro’s results statistically significant?

Is his group different from the rest of the population?

What can you conclude about MyFitnessPal and weight loss?

Do you believe Pedro’s findings to be substantively significant? (This question is not graded.)

Question 2

Professional athletes are often judged in terms of performance on a wide variety of metrics, which feed into a wide array of uses from fantasy leagues and gambling to AI development in video games. We will use the Fifa 2017 video game data as a proxy for actual soccer (football) player performance. Import the Fifa CSV file.

Mean composite scales are variables that represent a mean score built from other variables. Use R to construct a mean composite scale that represents a player’s kicking abilities. Use the following variables: Shot_Power, Finishing, Long_Shots, Curve, Freekick_Accuracy, Short_Pass, and Long_Pass. Attach your new variable to the Fifa 2017 dataset. See the R setup file for more on how to construct a mean composite scale with an example.

The following questions will have you evaluate the new kicking ability scores of all Fifa 2017 players, with those of the top and bottom ranked football clubs: FC Barcelona and Longford Town respectively. [ As reported using the top and bottom non-zero average rating on https://www.futhead.com/17/clubs/?sort=-average_rating. ] The club names are the same in the R data. You will need to use subsetting techniques to isolate the scores for each team. You may use whichever subsetting approach you prefer.

2a) Report summary statistics below for this new kick scale variable for all Fifa players, FC Barcelona, and Longford Town clubs (PQ table with n, min, max, 1st & 3rd quartiles, median, mean, and sd).

2b) Calculate a 95% confidence interval for the mean for all Fifa players, FC Barcelona, and Longford Town. Do this by hand below and confirm your work using R. Round your answer to 2 decimal places.

What can you conclude from your confidence intervals? Not graded, be descriptive.

Question 3

The General Social Survey has 6 questions that pertain to optimism. The variables are named LOTR1, LOTR2, LOTR3, LOTR4, LOTR5, and LOTR6.

3a) What type of variables are LOTR1 through LOTR6? (scale of measurement). Look up the full survey question asked for one of the six LOTR variables and enter it below. Hint: This will be easier if you use the GSS online data explorer rather than the pdf codebook.

3b) Use the R code from the setup file to create an item scale, which allows us to do quantitative analysis on it. The new variable will be called LOTR.scale. Examine the setup code to learn more about how we constructed this new variable.

Using a PQ table, report the n, min, max, 1st & 3rd quartiles, median, mean, and sd.

3c) Use R to produce a histogram of lotr.scale with PQ labels and title and insert it below this question. Describe if you believe the data to be relatively normally distributed.

3d) Use R to calculate a confidence interval for LOTR.scale. Use a = 0.01. Round your answer t

Sample Answer

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It is a unique cycle that oversees force, closeness and limits, cohesiveness and flexibility of route frameworks, and makes pictures, topics, stories, ceremonies, rules, jobs, making implications, making a feeling of family life An intelligent cycle that makes a model. This model has passed ages. Notwithstanding the view as a family and family automatic framework, one of the greatest exploration establishments in between family correspondence centers around a family correspondence model. Family correspondence model (FCP) hypothesis clarifies why families impart in their own specific manner dependent on one another ‘s psychological direction. Early FCP research established in media research is keen on how families handle broad communications data. Family correspondence was perceived as an exceptional scholastic exploration field by the National Communications Association in 1989. Family correspondence researchers were at first impacted by family research, social brain science, and relational hypothesis, before long built up the hypothesis and began research in a family framework zeroed in on a significant job. Until 2001, the primary issue of the Family Communication Research Journal, Family Communication Magazine, was given. Family correspondence is more than the field of correspondence analysts in the family. Examination on family correspondence is normally done by individuals in brain science, humanism, and family research, to give some examples models. However, as the popular family correspondence researcher Leslie Baxter stated, it is the focal point of this intelligent semantic creation measure making the grant of family correspondence special. In the field of in-home correspondence, correspondence is normally not founded on autonomous messages from one sender to one beneficiary, yet dependent on the dynamic interdependency of data shared among families It is conceptualized. The focal point of this methodology is on the shared trait of semantic development inside family frameworks. As such, producing doesn’t happen in vacuum, however it happens in a wide scope of ages and social exchange.

Standards are rules end up being followed when performing work to agree to a given objective. Hierarchical achievement relies significantly upon compelling correspondence. So as to successfully impart, it is important to follow a few standards and rules. Coming up next are rules to guarantee powerful correspondence: clearness: lucidity of data is a significant guideline of correspondence. For beneficiaries to know the message plainly, the messages ought to be sorted out in a basic language. To guarantee that beneficiaries can without much of a stretch comprehend the importance of the message, the sender needs to impart unmistakably and unhesitatingly so the beneficiary can plainly and unquestionably comprehend the data.>

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