Complete The Following Exercises From Review Questions Locat
Complete The Following Exercises From Review Questions Located At Th
Complete the following exercises from "Review Questions" located at the end of each chapter and put them into a Word document to be submitted as directed by the instructor. Chapter 1, numbers 1.8 and 1.9 Chapter 2, numbers 2.14, 2.17, and 2.18 Chapter 3, numbers 3.13, 3.14, 3.18, and 3.19 Chapter 4, numbers 4.9, 4.14, 4.17, and 4.19 Show all relevant work; use the equation editor in Microsoft Word when necessary.
Sample Paper For Above instruction
Complete The Following Exercises From Review Questions Located At Th
This paper addresses a selection of review questions from Chapters 1 through 4, focusing on understanding the nature of studies, data analysis, and basic statistical concepts. The questions span identifying study types, constructing frequency distributions, analyzing data variability, and applying statistical measures such as mean, median, and skewness. All solutions are detailed with relevant explanations and examples to exemplify key statistical principles essential for graduate-level understanding.
Chapter 1: Identifying Study Types and Confounding Variables
Question 1.8 explores the distinction between experimental and observational studies, asking whether specific studies fall into either category, and to identify variables involved.
Example (a): The psychologists' study on chimpanzees is an experiment because researchers assign chimpanzees to cages with varying crowd densities. The independent variable is the cage size or crowd level. Possible confounding variables include the age of chimps or individual temperaments, which might influence aggressive behavior independently of crowding.
Example (b): The study comparing birth astrological signs in artists versus scientists is observational, as there is no manipulation but an observation of existing data.
Example (c): The analysis based on records of tribes' sexual codes and their behavior is observational since the researcher is analyzing existing records rather than controlling variables.
Example (d): Assigning students to groups for problem-solving examines the effect of group size, making it an experiment, with independent variable being group size.
Example (f): Allowing students to choose between workshops and comparing subsequent GRE scores is observational due to self-selection, which may introduce bias.
Example (g): Relationship between attractiveness and anxiety scores is observational, correlational data without manipulation.
Example (h): Attitudes toward defense spending based on survey responses are observational and descriptive.
Question 1.9: Nature of Experimental and Observational Studies
Experiments manipulate independent variables, allowing causal inferences, while observational studies record existing variables without intervention. The breast-feeding studies involve both experimental (randomized controlled trials) and observational components, with potential confounders such as maternal education and socioeconomic status being controlled in experiments by randomization and in observational studies through statistical adjustments.
Chapter 2: Data Distributions and Percentiles
Question 2.14
(a) Constructed frequency distribution for number of residences:
- Values: 1, 4, 2, 3, 3, 1, 6, 7, 4, 3, 3, 9, 2, 4, 2, 2, 3, 2, 3, 4, 4, 2, 3, 3, 5
- Frequency distribution:
-
Number of Residences Frequency 1 2 2 6 3 7 4 5 5 1 6 1 7 1 9 1 - (b) The distribution tends to be right-skewed, with the majority occupying 2-3 residences and fewer with higher counts.
- Question 2.15
- The Facebook friends data shows a positively skewed distribution, as most users report fewer friends with a long tail extending toward higher numbers. Relative frequencies are calculated by dividing each frequency by the total (200). The approximate percentile for the interval 300-349 can be found by cumulative relative frequency; if it accounts for, say, 75% of the data, then its percentile rank is near 75th.
- Converting to stem-and-leaf is not feasible here due to data grouping.
- Question 2.16
- GPA data for meditators and non-meditators shows the unit of measurement as GPA points. Constructing frequency distributions involves choosing classes such as 1.0-2.0, 2.1-3.0, etc. Comparing the two distributions may reveal differences in central tendency or variability, but inferential tests are necessary for significance assessment.
- Question 2.17
- Skewness determination based on mean and median: a) incomes (mean > median) suggest positive skewness; b) GPAs with mean median) indicates positive skew; d) TV viewing times, mean
- Question 2.18
- Missing data calculations using the mean (5):
- (a): 1, 2, 10—missing value: 4
- (b): 2, 4, 1, 5, 7, 7—missing value: 3
- (c): 6, 9, 2, 7, 1, 2—missing value: 5
- Question 2.19
- Symbols: (a) N — population size; (b) varies — sample statistic; (c) S — sample standard deviation; (d) n — sample size; (e) constant — parameter; (f) subset — sample subset.
- Chapter 4: Variability and Comparative Distributions
- Question 4.9
- Standard deviations are compared: for example, SAT scores of high school seniors vs. college freshmen; typically, college freshmen may have a larger standard deviation due to wider age and preparedness variability.
- Question 4.14
- Verifying for weights: the sample standard deviation calculated as 23.33 lbs aligns with the weights provided. Most weights fall within one standard deviation of the mean, while some deviate beyond two.
- Question 4.17
- Distribution skewness based on mean and median: income (positive skew), GPA (negative skew), romantic affairs (positive skew), TV viewing (negative skew).
- Question 4.18
- For the set with mean 5: (a) missing value is 4; (b) missing value is 3; (c) missing value is 5.
- Question 4.19
- Population mean involves the symbol N; sample mean involves n; S refers to standard deviation; variables that vary are sample or population statistics; terms related to subsets refer to samples or populations as appropriate.