Category: Mathematics Homework Help

I have an assessment about the attached lectures, along with two other lectures I will be attaching. The assessment is on Wednesday 26 May and I need help solving it. The assessment will be an e-test where I will be providing you with a username and password to log in and solve it. There are specific rules regarding the platform the e-test will be held in such as syntax, I will be providing a file with all details. All answers must be correct and following the proper syntax. For practice, there will be a sample tutorial quiz in the same platform where you could login, solve it, use the validate option for each question to see if the syntax in the answer is correct. Solving the assessment should take no longer than 4 hours. DO NOT PRSS ON THE ‘MARK’ OPTION BECAUSE EACH TIME IT IS PRESSED ON A PARTIAL MARK WILL BE LOST.

• Continue using SAS on Demand for Academics Support Page resources that include video explanations and demonstrations:
  • Go to the SAS on Demand for Academic (Links to an external site.)
  • Click on Access nowlink in the middle of the page.
  • Go to Independent Learner – Step 2: Write Your SAS Programs.
  • Click on SAS Studio video library.
  • Under CATEGORIES tab, click on How To Tutorials.
  • See the video resources under SAS Studio.
• In this week problems students are supposed to compute probabilities for a given distribution or by table. In the first case, Distribution is given by name and students are supposed to remember the formula for that distribution from the name only. The formula is used instead of data to compute probabilities or SAS could compute the probability if name is used. In the second case, info from the table or data summaries may be used to compute desired probabilities.
  Students are supposed to figure out which case they are dealing with and proceed appropriately.

Excel Resources: https://statpages.info/javasta2.html#Excel (Links to an external site.)

R Resources: https://www.r-project.org/ (Links to an external site.)

• In this assignment, you are required to demonstrate your understanding of concepts, tools, and/or techniques covered in the course readings and class activities for this unit. You will demonstrate this through completion of selected practice exercises at the end of assigned chapter readings.
• You are required to be thorough in your responses. As you do this, constantly ask yourself (and reflect) on how you would apply the same statistical concepts and techniques to any other project of interest or to your upcoming dissertation. Even if the questions in the exercises are worded as yes/no questions, you are expected to provide an explanation and justification for your answers.

Do the following

• Based on your study of this unit’s assigned readings (Chapter 4, Sec 4.2 – 4.8, Exclude 5), discuss with your group members to complete the following practice exercises: (No attachment available, you have to provide it)
  • 16
  • 21
  • 23
  • 26
  • 41
  • 46
  • 51
• Type your answers with any supporting work on a Word document. If you use the SAS software to complete any statistical analysis in answering these practice questions, copy and paste the outputs into this document.

To whom it may concern,

Attached below is

– Screen shot of instructions for the project

– Sample of the EXCEL part of the project

– Sample of the DOCUMENT part of the project

Please carefully go over the sample of EXCEL and DOCUMENT part of the project first for better understanding.

1. Let me know the topic you decide to do the project on.

2. I need to report the decided topic to my teacher to validate the topic.

3. Proceed with the EXCEL and DOCUMENT.

Please ask me questions for clarification, thank you very much!

I pay well for amazing work.

Consider the following statistical output provided in the attachment. Explain the statistical significance of the correlation using the correlation coefficient. Explain the results in plain terms to someone who does not understand statistics.

	VAR00001	VAR00002
VAR00001 Pearson Correlation Sig. (2-tailed) N	1 8	-.242 .564 8
VAR00002 Pearson Correlation Sig. (2-tailed) N	-.242 .564 8	1 8

Question 1 (1 point)

Saved

Which of the following equations are linear?

Question 1 options:

	y+7=3x
	y=6x2+8
	3y=6x+5y2
	y-x=8x2

Question 2 (1 point)

Which of the following equations are linear?

Question 2 options:

	4y=8
	y2=6x3+8
	3y=6x+5y2
	y-x=8x2

Question 3 (1 point)

You decided to join a fantasy Baseball league and you think the best way to pick your players is to look at their Batting Averages.

You want to use data from the previous season to help predict Batting Averages to know which players to pick for the upcoming season. You want to use Runs Score, Doubles, Triples, Home Runs and Strike Outs to determine if there is a significant linear relationship for Batting Averages.

You collect data to, to help estimate Batting Average, to see which players you should choose. You collect data on 45 players to help make your decision.

x1 = Runs Score/Times at Bat

x2 = Doubles/Times at Bat

x3 = Triples/Times at Bat

x4 = Home Runs/Times at Bat

x5= Strike Outs/Times at Bat

Is there a significant linear relationship between these 5 variables and the Batting Average?

If so, what is/are the significant predictor(s) for determining the Batting Average?

See Attached Excel for Data.

Baseball data.xlsx

Question 3 options:

	No, Triples/Times at Bat, p-value = 0.291004037 > .05, No, Triples, are not a significant predictor for Batting Average. Home Runs/Times at Bat, p-value = 0.114060301 > .05, No, Home Runs are not a significant predictor for Batting Average.
	Yes, Triples/Times at Bat, p-value = 0.291004037 > .05, No, Triples, are not a significant predictor for Batting Average. Home Runs/Times at Bat, p-value = 0.114060301 > .05, No, Home Runs are not a significant predictor for Batting Average.
	No, Runs Score/Times at Bat, p-value = 0.000219186 < .05, Yes, Runs Score is a significant predictor for Batting Average. Doubles/Times at Bat, p-value = 0.00300543 < .05, Yes, Doubles are a significant predictor for Batting Average. Strike Outs/Times at Bat, p-value = 0.00000258892 < .05, Yes, Strikes Outs are a significant predictor for Batting Average.
	Yes, Runs Score/Times at Bat, p-value = 0.000219186 < .05, Yes, Runs Score is a significant predictor for Batting Average. Doubles/Times at Bat, p-value = 0.00300543 < .05, Yes, Doubles are a significant predictor for Batting Average. Strike Outs/Times at Bat, p-value = 0.00000258892 < .05, Yes, Strikes Outs are a significant predictor for Batting Average.

Question 4 (1 point)

You are thinking about opening up a Starbucks in your area but what to know if it is a good investment. How much money do Starbucks actually make in a year? You collect data to, to help estimate Annual Net Sales, in thousands, of dollars to know how much money you will be making.

You collect data on 27 stores to help make your decision.

x1 = Rent in Thousand per month

x2 = Amount spent on Inventory in Thousand per month

x3 = Amount spent on Advertising in Thousand per month

x4 = Sales in Thousand per month

x5= How many Competitors stores are in the Area

Approximately what percentage of the variation in Annual Net Sales is accounted for by these 5 variables in this model?

See Attached Excel for Data.

Starbuck Sales data.xlsx

Question 4 options:

	27.62% of variation in the Annual Net Sales is accounted for by Rent, Inventory, Advertising, Sales per Month and # of Competitor store in this model.
	98.24% of variation in the Annual Net Sales is accounted for by Rent, Inventory, Advertising, Sales per Month and # of Competitor store in this model.
	99.11% of variation in the Annual Net Sales is accounted for by Rent, Inventory, Advertising, Sales per Month and # of Competitor store in this model.
	99.25% of variation in the Annual Net Sales is accounted for by Rent, Inventory, Advertising, Sales per Month and # of Competitor store in this model.

Question 5 (1 point)

With Obesity on the rise, a Doctor wants to see if there is a linear relationship between the Age and Weight and estimating a person’s Systolic Blood Pressure. Is there a significant linear relationship between Age and Weight and a person’s Systolic Blood Pressure?

If so, what is/are the significant predictor(s) for Systolic Blood Pressure?

See Attached Excel for Data.

BP data

Question 5 options:

	No, Age, p-value = 0.9388 > .05, No, Age is not a significant predictor for Systolic BP Weight, p-value = 0 .3092 > .05, No, Weight is not a significant predictor for Systolic BP
	No, Age, p-value = 0.001303023 < .05, No, Age is not a significant predictor for Systolic BP Weight, p-value = 0.023799395 < .05, No, Weight is not a significant predictor for Systolic BP
	Yes, Age, p-value = 0.9388 > .05, Yes, Age is a significant predictor for Systolic BP Weight, p-value = 0 .3092 > .05, Yes Weight is a significant predictor for Systolic BP
	Yes, Age, p-value = 0.001303023 < .05, Yes, Age is a significant predictor for Systolic BP Weight, p-value = 0.023799395 < .05, Yes Weight is a significant predictor for Systolic BP

Question 6 (1 point)

You move out into the country and you notice every Spring there are more and more Deer Fawns that appear. You decide to try and predict how many Fawns there will be for the up coming Spring.

You collect data to, to help estimate Fawn Count for the upcoming Spring season. You collect data on over the past 10 years.

x1 = Adult Deer Count

x2 = Annual Rain in Inches

x3 = Winter Severity

• Where Winter Severity Index:
  • 1 = Warm
  • 2 = Mild
  • 3 = Cold
  • 4 = Freeze
  • 5 = Severe

Estimate Fawn Count when Adult Deer Count = 10, Annual Rain = 13.5 and Winter Severity = 4

See Attached Excel for Data.

Deer data.xlsx

Question 6 options:

	5
	3.85
	3.06
	2.95

Question 7 (1 point)

In the context of regression analysis, what is the definition of an influential point?

Question 7 options:

	Observed data points that are far from the least squares line
	Observed data points that are far from the other observed data points in the horizontal direction
	Observed data points that are close to the least squares line
	Observed data points that are close to the other observed data points in the horizontal direction

Question 8 (1 point)

The least squares regression line for a data set is yˆ=2.3−0.1x and the standard deviation of the residuals is 0.13.

Does a case with the values x = 4.1, y = 2.34 qualify as an outlier?

Question 8 options:

	Yes
	No
	Cannot be determined with the given information

Question 9 (1 point)

The following data represent the weight of a child riding a bike and the rolling distance achieved after going down a hill without pedaling.

Weight (lbs.)	Rolling Distance (m.)
59	26
84	43
97	48
56	20
103	59
87	44
88	48
92	46
53	28
66	32
71	39
100	49

Can it be concluded at a 0.05 level of significance that there is a linear correlation between the two variables?

Question 9 options:

	yes
	no
	Cannot be determined

Question 10 (1 point)

A negative linear relationship implies that larger values of one variable will result in smaller values in the second variable.

Question 10 options:

	True
	False

Question 11 (1 point)

You determine there is a strong linear relationship between two variables using a test for linear regression. Can you immediately claim that one variable is causing the second variable to act in a certain way?

Question 11 options:

	No, the correlation would need to be a perfect linear relationship to be sure.
	No, you should examine the situation to identify lurking variables that may be influencing both variables.
	No, you must first decide if the relationship is positive or negative.
	Yes, a strong linear relationship implies causation between the two variables.

Question 12 (1 point)

Which of the following describes how the scatter plot appears? Select all that apply.

Question 12 options:

	positive
	negative
	neither positive or negative

Question 13 (1 point)

The following data represent the weight of a child riding a bike and the rolling distance achieved after going down a hill without pedaling.

Weight (lbs.)	Rolling Distance (m.)
59	26
84	43
97	48
56	20
103	59
87	44
88	48
92	46
53	28
66	32
71	39
100	49

Regression Statistics
Multiple R	0.956806
R Square	0.915477
Adjusted R Square	0.907025
Standard Error	3.483483
Observations	12

ANOVA
	df	SS	MS	F	Significance F
Regression	1	1314.32	1314.32	108.3113	1.1E-06
Residual	10	121.3466	12.13466
Total	11	1435.667

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-8.56472	4.7892	-1.78834	0.104007	-19.2357	2.106284
Weight (lbs.)	0.611691	0.058775	10.40727	1.1E-06	0.480731	0.742651

Find the standard error of estimate. Round answer to 4 decimal places.

___

Question 13 options:

Question 14 (1 point) Body frame size is determined by a person’s wrist circumference in relation to height. A researcher measures the wrist circumference and height of a random sample of individuals. Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .734a .539 .525 4.01409 a. Predictors: (Constant), Wrist Circumference b. Dependent Variable: Height ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 621.793 1 621.793 38.590 .000b Residual 531.726 33 16.113 Total 1153.519 34 a. Dependent Variable: Height b. Predictors: (Constant), Wrist Circumference Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 38.177 5.089 7.502 .000 Wrist Circumference 4.436 .714 .734 6.212 .000 What is the value of the test statistic to see if the correlation is statistically significant? Question 14 options: 0.539 7.502 4.436 5.089 6.212 0.734 Question 15 (1 point) What are the hypotheses for testing to see if a correlation is statistically significant? Question 15 options: H0: ρ = 0 ; H1:ρ =1 H0: ρ = ±1 ; H1:ρ ≠ ±1 H0: r = 0 ; H1: r ≠ 0 H0: ρ = 0 ; H1:ρ ≠ 0 H0: r = ±1 ; H1: r ≠ ± 1 Question 16 (1 point) A teacher believes that the third homework assignment is a key predictor in how well students will do on the midterm. Let x represent the third homework score and y the midterm exam score. A random sample of last terms students were selected and their grades are shown below. Assume scores are normally distributed. Calculate the correlation coefficient using technology (you can copy and paste the data into Excel). Round answer to 4 decimal places. Make sure you put the 0 in front of the decimal. HW3 Midterm 13.1 59.811 21.9 87.539 8.8 53.728 24.3 95.283 5.4 39.174 13.2 66.092 20.9 89.729 18.5 78.985 20 86.2 15.4 73.274 25 93.25 9.7 52.257 6.4 43.984 20.2 79.762 21.8 84.258 23.1 92.911 22 87.82 11.4 54.034 14.9 71.869 18.4 76.704 15.1 70.431 15 65.15 16.8 77.208 Answer:___ ___ Question 16 options: Question 17 (1 point) Choose the correlation coefficient that is represented in the scatterplot. Question 17 options: 0.83 0.15 -0.82 Question 18 (1 point) The correlation coefficient, r, is a number between: Question 18 options: 0 and ∞ -10 and 10 -∞ and ∞ 0 and 10 0 and 1 – 1 and 1 Question 19 (1 point) A teacher believes that the third homework assignment is a key predictor in how well students will do on the midterm. Let x represent the third homework score and y the midterm exam score. A random sample of last terms students were selected and their grades are shown below. Assume scores are normally distributed. HW3 Midterm 13.1 59.811 21.9 87.539 8.8 53.728 24.3 95.283 5.4 39.174 13.2 66.092 20.9 89.729 18.5 78.985 20 86.2 15.4 73.274 25 93.25 9.7 52.257 6.4 43.984 20.2 79.762 21.8 84.258 23.1 92.911 22 87.82 11.4 54.034 14.9 71.869 18.4 76.704 15.1 70.431 15 65.15 16.8 77.208 Find the y-intercept and slope for the regression equation using technology (you can copy and paste the data into Excel). Round answer to 3 decimal places. ŷ=___+___x Question 19 options: Blank # 1 Blank # 2 Question 20 (1 point) Bone mineral density and cola consumption has been recorded for a sample of patients. Let x represent the number of colas consumed per week and y the bone mineral density in grams per cubic centimeter. Assume the data is normally distributed. A regression equation for the following data is ŷ=0.8893-0.0031x. Which is the best interpretation of the slope coefficient? x y 1 0.883 2 0.8734 3 0.8898 4 0.8852 5 0.8816 6 0.863 7 0.8634 8 0.8648 9 0.8552 10 0.8546 11 0.862 Question 20 options: For every additional average weekly soda consumption, a person’s bone density increases by 0.0031 grams per cubic centimeter. For every additional average weekly soda consumption, a person’s bone density decreases by 0.0031 grams per cubic centimeter. For an increase of 0.8893 in the average weekly soda consumption, a person’s bone density decreases by 0.0031 grams per cubic centimeter. For every additional average weekly soda consumption, a person’s bone density decreases by 0.8893 grams per cubic centimeter.

For this assignment, I would like you to write a 3 to 6-page paper (not including title and reference page) on one of the following topics: 

• The impact of depression on college students
• The impact of anxiety on college students
• Factors that prevent a student from succeeding and persisting on to graduating from college
• You can come up with your own topic, but you must get permission from me before writing on it. 

Example of Paper Components using the first topic:

Title: Depression Among College Students and its Impact on Academic Success

1. Introduction (2-3) paragraphs
   1. Discuss the demographics of college students (the age they usually attend, how many are attending, which schools they are selecting (community schools or living on campus at larger schools)
   2. Emerging Adulthood (18-24 years old)- discuss this period, what are they usually experiencing during this time period?
   3. What is the current research on the topic of depression among college students and how this impacts their academic success? How prevalent is depression among those in this age group? 
2. Depression 
   1. Depression in America as a whole (define it and discuss causes)
   2. How does depression affect college students (traditional 18-24-year-old) different than the average person (hint, this age group may not show all of the classic symptoms, yet they can still be diagnosed with depression)?
   3. How does depression affect men and women differently?
3. What does the research say about depression on College Campuses? 
   1. Who is it impacting on the campus?
   2. How is it being diagnosed?
   3. Are they receiving treatment at student health centers?
   4. What does the research say about the relationship between depression among college students and academic outcomes?
4. Treatment
   1. Clinical treatment for depression
   2. How are college health centers treating, or are they treating?
5. Academic Success
   1. What is academic success, and how is it measured in college. 
   2. How long is it taking the average college student to complete a four-year degree?
   3. What percentage of first-year college students are traditional (18 to 24 years old), nontraditional (began the first year of college after the age of 24 or perhaps they are married and/have children), and how many are first-generation college students. You can discuss how each might relate to academic success, as outlined in the literature. 
6. Conclusion (1-2 paragraphs)
   1. Relate back to intro (story, quote, question)
   2. Restate thesis/clincher

I’m stuck on a Statistics question and need an explanation.

Q1/ The table below shows the results from the Student Data Survey regarding home residence with regard to class year status. Underclassman means first year or sophomore; upperclassman means junior or senior.

	Underclassman	Upperclassman
From Florida	7	2
From state other than FL	5	2
International	1	1

Suppose that one student is chosen at random from the class. Based on the table above, find the probability that the student is……. (Note: please leave answers as fractions reduced to lowest terms).

a. from Florida

b. from Florida and an upperclassman

c. from Florida or an upperclassman

d. from Florida, given that the student is an upperclassman

Q2/ The last question on the Student Data Survey asked you to flip a coin 10 times and record the number of heads you got.

a. If you flip a coin one time, what is the probability of heads?

b. If you flip a coin 10 times, based on the theoretical probability in part a, how many heads would you expect?

c. Here are the results from the Survey: 7 6 5 4 6 4 6 4 6 6 4 7 6 4 6 5 4 6 . What is the mean number of heads recorded, based on this experiment?

Q3/ In statistics, a binomial experiment is one which can have only two outcomes. Flipping a coin one time is binomial, since there are only two outcomes – heads or tails.

Which of these variables from the Student Data Survey are binomial?

a. do you live in campus-owned housing?

b. what color are your eyes?

c. What is your class year?

This is a multiple linear regression project. Please complete

1) pairwise plot, 2) usefulness of the model, 3) model diagnostics, significance of each individual predictor, 4) model
selection, 5) partial F-test, 6) VIF and multicollinearity analysis, 7) need for transformation, examples or estimation and predictions including confidence intervals, 8) interpretation of your results including coefficient estimates, 9) enhanced scatterplot related to your dummy variable, 10) the plot of R2-adjusted for all models under consideration in the model selection, and 11) other tools that you may suggest.

For the full model: price = mileage + year + mpg + tax + engineSize + transmission + fuelType + brand

I need RMD file

I don’t understand this Statistics question and need help to study.

Please cited any named theorem used 

(for ex: Weak Law of Large Numbers, Central Limit Theorem, etc)