Mathematics Homework Help

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W6 Domain and Range of The Functions Discussion

 

I’m working on a algebra project and need support to help me learn.

b. Find the domain and range of the functions f open parentheses x close parentheses and g open parentheses x close parentheses. Show how you found them or explain how you determined them. Post the (labeled) domain and range as a correctly formatted interval.*

c. Identify any x-intercepts

d. Identify any y-intercepts.

d. Identify the equation(s) of any vertical or horizontal asymptotes. Label them appropriately.

e. Post an image of the graph of your function.

4

f open parentheses x close parentheses equals square root of x minus 4 end root plus 3

g open parentheses x close parentheses equals negative 2 x plus 4

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Saint Marys University Compute the Values Linear Algebra MCQs

 

This is the sample question about the Linear, this homework about the Matrix, this includes the multiple-choice and the long question. it is not hard.

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Rowan University The Host of A Birthday Party Discussion

 

I’m working on a mathematics discussion question and need an explanation and answer to help me learn.

Consider the following word problem: “The host of a birthday party knows that 4/3 of a quart of juice will be enough for 2/5 of the children at the party. If the same amount of juice is given to each child, what is the total amount of juice needed for all of the children at the party?”

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University of Toronto Exponents Logarithmic & Trigonometric Functions Questions

 

Only accept if you are available to help me for the next 3 hours solving a math assignment I would need you to solve each question and send it to me one after the other there should be no more than 18 high school grade questions from the topics of Basic Fundamentals, Functions, Polynomial and Rational Functions, Exponents and Logarithmic Functions and Trigonometric functions I’ll be attaching previous few quizzes in chat to explain a little more but Ill need you to be with me for next at least 3 hours.

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DePaul University Earthworm & Plant Biomass Lab Report

 

Earthworm & Plant Biomass Lab Report (Lab 4), Rubric amended S2021

Results /45

1)contains all relevant data and statistical tests

2) summary data tables and/or figures (graphs) and statistics.

3)tables and/or figures completely labeled and have figure legends and clear CAPTION.

4)Contains summaries of results

Discussion /50

1)brief summary of the results and relevance is clear

2)interpretation of results and development relative to other similar scientific studies strong (do results challenge an existing view, open up a new area of investigation, or support previous results? This section needs to be expansive and include the flow diagram for conceptual purposes. You need to connect the factors/parameters with evidence. Direct and indirect effects must be accounted for.

3)next steps of research suggested

4)limitations clear (how much confidence do you have in your results based on statistical tests, number of replications, etc.?)

5)possible implications of your work for “real world” applications clear

6)at least 3 relevant references cited and referred to intelligently

Literature Cited /3

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Post Sampling Distributions Important to The Study of Inferential Statistics Ques

 

Why are sampling distributions important to the study of inferential statistics? In your answer, demonstrate your understanding by providing an example of a sampling distribution from an area such as business, sports, medicine, social science, or another area with which you are familiar. Remember to cite your resources and use your own words in your explanation.

must be 100 words

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SNHU Data Analysis Hypothesis Testing Statistics Questions

 

Use the Sun Coast Remediation data set to conduct a correlation
analysis, simple regression analysis, and multiple regression analysis
using the correlation tab, simple regression tab, and multiple
regression tab respectively. The statistical output tables should be cut
and pasted from Excel directly into the final project document. For the
regression hypotheses, display and discuss the predictive regression
equations if the models are statistically significant. Delete
instructions and examples highlighted in yellow before submitting this
assignment.

Correlation: Hypothesis Testing

Restate the hypotheses from Unit II here.

Example:

Ho1: There is no statistically significant relationship between height and weight.

Ha1: There is a statistically significant relationship between height and weight.

Enter data output results from Excel Toolpak here.

Interpret and explain the correlation analysis results below the Excel output. Your explanation should include: r, r2, alpha level, p value, and rejection or acceptance of the null hypothesis and alternative hypothesis.

Example:

The Pearson correlation coefficient of r = .600 indicates a moderately strong positive correlation. This equates to an r2 of .36, explaining 36% of the variance between the variables.

Using an alpha of .05, the results indicate a p value of
.023 < .05. Therefore, the null hypothesis is rejected, and the
alternative hypothesis is accepted that there is a statistically
significant relationship between height and weight.

Note: Excel data analysis Toolpak does not automatically calculate the p
value when using the correlation function. As a workaround, the data
should also be run using the regression function. The Multiple R is identical to the Pearson r in simple regression, R Square is shown, and the p value is generated. Be sure to show your results using both the correlation function and simple regression function.

Simple Regression: Hypothesis Testing

Restate the hypotheses from Unit II here.

Ho2:

Ha2:

Enter data output results from Excel Toolpak here.

Interpret and explain the simple regression analysis results below the Excel output. Your explanation should include: multiple R, R squared, alpha level, ANOVA F value, accept or reject the null and alternative hypotheses for the model, statistical significance of the x variable coefficient, and the regression model as an equation with explanation.

Multiple Regression: Hypothesis Testing

Restate the hypotheses from Unit II here.

Ho3:

Ha3:

Enter data output results from Excel Toolpak here.

Interpret and explain the simple regression analysis results below the Excel output. Your explanation should include multiple R, R squared, alpha level, ANOVA F value, accept or reject the null and alternative hypotheses for the model, statistical significance of the x variable coefficients, and the regression model as an equation with explanation.

References

Include references here using hanging indentations. Remember to remove this example.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.

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Colorado State University Week 8 Exponential and Logarithmic Equations Questions

 

Welcome to Module 8! Last week we studied and analyzed exponential and logarithmic equations and their applications. We also reviewed exponential growth and decay models and applied them to real-world contexts.

In Module 8, we will focus on solving systems of two equations in two variables. We will discuss three different approaches for solving this type of system of equations. Additionally, we will learn how to solve systems of three equations in three variables using Gaussian elimination.

Note 1:
Please make sure you review all the previous comments before you submit your post, to avoid identical examples or work.

Note 2:
As I mentioned in other posts, it would be ideal if you can submit your peer responses on different days, after your initial post. The order is important, as I need time to review your initial posts, and you need to remain active, and visible, on the discussion board, throughout the entire week.

Note 3:
Please post and mark your answers in the given task order (1, 2, and 3) for an easy review and understanding of your assignment work. Please show detailed work and explanations instead of final answers.

Learning Outcomes:

  1. Solve systems of linear equations in two variables by graphing.
  2. Solve systems of linear equations in two variables by substitution.
  3. Solve systems of linear equations in two variables by elimination.
  4. Solve systems of linear equations in three variables with Gaussian elimination.
  5. Solve application problems involving systems of equations.

Make sure you read Sections 7.1, 7.2, and 7.6 in the course textbook and follow the examples outlined there. Also, work through the examples shown in the additional required readings. Practice makes perfect!

Next, attempt the Opening Exercises before engaging with the personalized lecture where you will complete Check Your Understanding that will help you navigate the various sections. Once you feel ready, you can attempt the Mastery Exercises that can be found in Knewton alta.

This week you will also have a final exam. Set aside some time to complete the final exam review (preview) in Knewton alta this week, so you are prepared for the final exam due at the end of this week.

Required materials:

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Your task for this discussion is as follows:

  1. Write a system of two linear equations that model something from your daily life.
  2. Solve the system of equations in two ways.
  3. Discuss which method you liked better and why.
  4. In your responses to peers, compare and contrast your preferences for how to solve systems of equations.

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Colorado University Application of Correlation & Regression Discussion

 

Your task for this discussion is as follows:

  1. Use the internet to find a website that shows an example or  application of correlation or regression in an area of interest in your  personal or professional life.
  2. Discuss how correlation or regression was used, summarize your findings, and share them.
  3. Be sure to include the independent and dependent variable – discuss the impact/relevance of the independent variable.

here is an example post:

Hello Class,

For this week’s discussion I was intrigued by a real life example from a case study of SAT and College GPA scores that used a linear regression analysis. The study involved examining high school grades (GPA) of students to predict college performance (GPA) of 105 computer science majors. According to Holmes et al. (2017), regression analysis is a valuable method used to determine whether or not a cause and effect relationship exists and also measures the magnitude of that relationship. Hence, a scatter plot and approximation with a line of best fit is developed from the data points that can be represented by a simple linear equation, Y’=bX + A. A simple linear regression refers to a method for studying the association between two variables where one variable is the predictor or independent variable (known as the explanatory variable, X), and the other is the dependent variable, Y (Gopalan, 2020). Thus, a change in one variable can be used to predict the effects on another.

Source: University GPA as a function of High School GPA (Lane, n.d.).

In order to study the relationship between high school GPA and University GPA, a scatter plot was constructed (displayed above). It was determined that there was also a strong positive relationship among the variables with a correlation of 0.78 (Lane, n. d.). The regression equation used in the case study was Y’=bX + A: “University GPA’ = (0.675)(High School GPA) + 1.097, where, Y` denotes the predicted value (University GPA) , b denotes the slope of the line (0.675), X denotes the independent variable (High School GPA), and A is the Y-intercept (1.097). Thus, a student with a 3.0 H.S. GPA was predicted to have a 3.12 GPA in the University [University GPA’=(0.675)(3.0)+1.097=3.12] (Lane, n. d.). The study proved that there was a strong positive relationship between the prior and future performance of the ‘observed’ sample of students. Holmes et al (2017) states that when X and Y have a positive linear relationship, an increase in X, independent variable, will increase Y, the dependent variable. In other words, the impact of higher high school GPA had a positive impact on their college GPA. Thus, we could infer that there is a strong relationship between the variables and students that did well in high school will be expected to do well in college.