## BTM8107-8 Week 5 Activity Apply ANCOVA & Factorial ANOVA

BTM8107-8 Week 5 Activity Apply ANCOVA & Factorial ANOVA in \$39 Only

You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into Word.
This assignment consists of two parts. In the first part, you will utilize an existing dataset to compute a factorial ANOVA. All SPSS output should be pasted into your Word document. In the second part, you will be asked to create a hypothetical ANCOVA output table for variables related to your area of research interest.

## MAT 300 Homework

MAT 300 Homework for \$39 Only (Sample Answer)

All calculations and relevant Minitab output must be included to receive full credit. Be sure to word-process your solutions and copy and paste the appropriate outputs from Minitab. Show all steps used in arriving at the final answers. Incomplete solutions will receive partial credit. This exam covers content from Modules One through Three.

(1) Consider the GASTURBINE data set and corresponding output from Minitab. Note that all tests should be performed at the α = 0.05 level. Use the complete data set in your analysis. The first 10 observations are given for illustrative purposes. Complete parts a) through f) below.

## STAT 200 Week 6 Homework

STAT 200 Week 6 Homework in \$25 Only
18. You choose an alpha level of .01 and then analyze your data.
a. What is the probability that you will make a Type I error given that the null hypothesis is true?
b. What is the probability that you will make a Type I error given that the null hypothesis is false?

7. Below are data showing the results of six subjects on a memory test. The three scores per subject are their scores on three trials (a, b, and c) of a memory task.
Are the subjects get- ting better each trial? Test the linear effect of trial for the data.

## STAT 200 Week 7 Homework and Quiz 3

STAT 200 Week 7 Homework and Quiz 3 in \$45 Only

Lane – Ch. 14

2. The formula for a regression equation is Y’ = 2X + 9.

a. What would be the predicted score for a person scoring 6 on X?

b. If someone’s predicted score was 14, what was this person’s score on X?

6. For the X,Y data below, compute:

a. r and determine if it is signiﬁcantly different from zero.

b. the slope of the regression line and test if it differs signiﬁcantly from zero.

c. the 95% conﬁdence interval for the slope.

## STAT 200: Introduction to Statistics Final Examination

STAT 200: Introduction to Statistics Final Examination, Spring 2016 OL1/US1 in \$79 Only

STAT 200: Introduction to Statistics Final Examination, Spring 2016 OL1/US1

1. True or False. Justify for full credit.
(a) The standard deviation of a data set cannot be negative.
(b) If P(A) = 0.4 , P(B) = 0.5, and A and B are disjoint, then P(A AND B) = 0.2.
(c) The mean is always equal to the median for a normal distribution.
(d) A 95% confidence interval is wider than a 98% confidence interval of the same parameter.
(e) In a two-tailed test, the value of the test statistic is 1.5. If we know the test statistic follows a Student’s t-distribution with P(T < 1.5) = 0.98, then we fail to reject the null hypothesis at 0.05 level of significance .
2. Identify which of these types of sampling is used: cluster, convenience, simple random, systematic, or stratified. Justify for full credit.

## MAT540 Week 8 Assignment 1 Linear Programming Case Study

MAT540 Week 8 Assignment 1 Linear Programming Case Study in \$15 Only

Assignment 1. Linear Programming Case Study
Your instructor will assign a linear programming project for this assignment according to the following specifications.
It will be a problem with at least three (3) constraints and at least two (2) decision variables. The problem will be bounded and feasible. It will also have a single optimum solution (in other words, it won’t have alternate optimal solutions). The problem will also include a component that involves sensitivity analysis and the use of the shadow price.
You will be turning in two (2) deliverables, a short writeup of the project and the spreadsheet showing your work.
Writeup.

## Qnt. 5040 HCT Merger Forecasting Case Study

Qnt. 5040 HCT Merger Forecasting Case Study in \$36 Only

HCT Merger Forecasting Case Study Qnt. 5040

Forecasting Case Study
Maximum Points: 35 points
Files Needed:
1. HCT Merger Forecasting Case Study 2015 (a Word file)
2. HCT Forecasting Case Study Spring 2015 Raw Data (an Excel file)
Introduction
This is the second of the major reports that you will completing this term. This individual case study is a forecasting study, you are to analyze the data about the company using the various statistical tests you have previously learned in this course and then forecast the next 8 months of revenue for the HCT multinational corporation (http://www.htc.com/us/). Then you will advise Mr. Wallbanger if he should proceed with the possible purchase of 4 percent of the company for \$250 million (US).

## Question Related to Confidence Intervals

Question Related to Confidence Intervals in \$8 Only

Confidence Intervals” (Note: Please respond to one [1] of the following two [2] bulleted items)

The vast majority of the world uses a 95% confidence in building confidence intervals. Give your opinion on why 95% confidence is so commonplace. Justify your response.
Construct a hypothetical 95% confidence interval for a hypothetical case of your choosing. Use your own unique choice of mean, standard deviation, and sample size to calculate the confidence interval. Select one (1) option provided below and analyze what will happen to your confidence interval based on the option you selected:
The confidence changes to 90%.
The confidence changes to 99%.
The sample size is cut in half.
The sample size is doubled.
The sample size is tripled.
Price of Answer: Just US\$8 only

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## CSE 340 The transistor Used

CSE 340 The transistor Used in \$8 only

b) The transistor used in the circuit below has the following h-parameters, hie = 2k; hoe = 60

S; hfe = 100.Calculate:

i) The amplifier current gain [5mks]

ii) The actual power delivered to the external load [3mks]

iii) The turn’s ratio required for a matching transformer in order to maximize the power