STAT 263 Final: Final Exam - Fall 2017

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cerisegiraffe538

1 Mar 2018

School

Queen's University

Department

Statistics

Course

STAT 263

Professor

Charles Arthur Molson

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Document Summary

Lurking variable/ confounding factor: a variable that has an important effect on the relationship among the variables but is not included in those variables. Operational definition: a clear, concise, detailed definition of a measure, fundamental for data collection. Population: set of data consisting of all possible observations. Nominal data: gives name and # to individuals or mutually exclusive categories. Is categorical but appears numerical: exclusive if occurrence of a precludes b or c. Ordinal data: mutually exclusive categories w a fixed order ex. Letter grades: only valid for inequalities no other arithmetic. Interval data: mutually exclusive, w a fixed order, and equal spacing b/n categories ex. Celsius and fahrenheit scale: can use inequalities but cannot multiply or divide. Ratio data: mutually exclusive, w a fixed order, equal spacing b/n categories and w an abs zero point ex. height, weight: can form inequalities, multiple and divide.

Related Questions

A survey in 2010 showed that college students study for a statistics exam on average for three hours and ten minutes. To determine if the average time is changed for the statistics exam data gathered from 81 students in a Midwest University. The sample showed that the average studying time for a statistics exam is two hours and fifty minutes with a standard deviation of one hour and thirty minutes. Let .

a. State the null and alternative hypothesis to be tested.

b. Identify the level of significance.

c. Identify the test statistics.

d. Determine the critical value for this test and formulate a decision rule using the critical value.

e. Compute the test statistics.

f. What do you conclude? Interpret the results.

copperfly458

Grades A Statistics instructor created a linear regression equation to predict students' final exam scores from their midterm exam scores. The regression equation was

(a) If Susan scores a 70 on the midterm, what did the instructor predict for her score on the final?

(b) Susan got an 80 on the final How big is her residual?

(c) If the standard deviation of the final was 12 points and the standard deviation of the midterm was 10 points, what is the correlation between the two tests?

(d) How many points would someone need to score on the midterm to have a predicted final score of 100?

orangehornet137

Module 4 Minitab Assignment

Instructions:

Below you will find questions. Please use the provided data to complete the tasks and to answer the questions.

For the standard normal distribution, Z, find the proportion (percentage) of observations that satisfies each of the following statements. In each case, use a graph of the Normal curve and shade the area under the curve that is the answer to the question.

Values of z larger than 2.33.

Between z = −1.50 and z = 1.18.

Values of z less than 1.19.

A set of final examination grades in an introductory statistics course was found to be normally distributed with a mean of 77 and a standard deviation of 8. For each of the following produce a normal distribution plot showing the appropriate shaded area.

What percentage of students scored lower than 70 on this exam?

What percentage of students scored between 81 and 89?

What percentage of students scored higher than 90 on this exam?

What is the 80th percentile of scores?

What is the 95th percentile of scores?