I need help with a mathematical powerpoint presentation

The purpose of this assignment is to use skills you have learned throughout all five weeks of the course. You will apply what you have learned to solve a real-world scenario that you could encounter in your future career.Create a 3- to 4-slide Microsoft® PowerPoint® presentation including:One slide on your topic and scenario (approximately 1 minute)Introduce the topic and scenario you selected.Explain why you are interested in the topic and scenario you chose. One to two slides of your visuals (approximately 2 to 3 minutes) These should be clear, neat, organized, and labeled.Explain why either the linear model or the exponential model is better for predicting future results. In your explanation, include the significance of the R2 value and how the R2 value influenced the model you chose.Explain how your visuals support your conclusion.One slide for a conclusion (approximately 1 minute)Restate your topic and scenario and give your findings for the scenario.Discuss how your topic and scenario relates to a real-world scenario.Discuss what you learned from this project.Include detailed speaker notes for each slide.I have attached Part 1 of the assignment from last week for reference as well as my Business Scenario which states why I chose this topic.Thanks in advance.
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Signature Assignment Presentation
The purpose of this assignment is to use skills you have learned throughout all five weeks of the
course. You will apply what you have learned to solve a real-world scenario that you could encounter
in your future career.
Create a 3- to 4-slide Microsoft® PowerPoint® presentation including:
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?
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One slide on your topic and scenario (approximately 1 minute)
o Introduce the topic and scenario you selected.
o Explain why you are interested in the topic and scenario you chose.
One to two slides of your visuals (approximately 2 to 3 minutes)
o These should be clear, neat, organized, and labeled.
o Explain why either the linear model or the exponential model is better for predicting
future results. In your explanation, include the significance of the R2 value and how
the R2 value influenced the model you chose.
o Explain how your visuals support your conclusion.
One slide for a conclusion (approximately 1 minute)
o Restate your topic and scenario and give your findings for the scenario.
o Discuss how your topic and scenario relates to a real-world scenario.
o Discuss what you learned from this project.
Include detailed speaker notes for each slide.
Types of Assignments
Week 1
Objectives/Competencies
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1.1Convert between percentages, decimals and fractions.
1.2Perform addition, subtraction, multiplication and division on fractions.
1.3Solve problems using order of operations.
1.4Determine the solution to square root problems.
1.5Solve real-world application problems using mathematical fundamentals.
Week 2
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Evaluate exponential equations using the laws of exponents.
2.2Use estimations to determine solutions to problems.
2.3Use basic laws of exponents and scientific notation to solve problems.
2.4Solve application problems.
Week 3
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Determine the ratio of the given data.
3.2Use dimensional analysis of fractions to convert units.
3.3Use percentages to describe change.
3.4Use graphs to determine ratios.
3.5Use proportional reasoning to determine percentage-based outcomes.
3.6Use appropriate problem solving techniques to solve application problems that have
proportional and non-proportional relationships.
3.7Solve application problems using proportional reasoning skills.
Week 4
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Describe the difference between linear and exponential functions.
4.2Analyze linear and exponential functions.
4.3Create linear and exponential models from data.
4.4Use linear and exponential models to draw conclusions.
4.5Explain the meaning of intercept in real-world scenarios.
4.6Use technology to calculate linear and exponential regression.
4.7Solve application problems.
Week 5
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Use Sets and Venn Diagrams to solve problems.
5.2Analyze data using Venn Diagrams.
5.3Rearrange formulas to isolate a specific variable.
5.4Solve for a variable in a given equation.
5.5Analyze arguments to determine validity.
5.6Solve real-world scenarios using quantitative reasoning skills.
Signature Assignment: Selecting a Topic
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Due Feb 05, 11:59 PM
o Not Submitted
o POINTS 3
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Paper
Objectives:
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3.1
3.2
3.3
more
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Instructions
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Assignment Files
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Grading
The purpose of this assignment is to get you to think about what types of problems you will be
solving in your career using data. In this assignment, you choose a topic and the associated problem
to solve. You will solve the problem throughout the rest of this course.
Select a topic with your data set from the Signature Assignment Topics and Scenarios . After
opening the file, access the topics by clicking the tabs at the bottom of the Excel® spreadsheet. You
have six topics to choose from that align to various career disciplines.
Review the data sets help if you are struggling to locate all of the data sets.
Answer the following questions using at least 175 words:
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What topic did you choose?
Why does it interest you?
What do you hope to discover in your analysis?
Click the Assignment Files tab to submit your assignment.
Business Scenario
Scenario 5
Month
1
2
3
4
5
6
7
8
9
10
11
Predicting Fuji Apple Purchases
Review the monthly data involving Fuji apples purchased at a large grocery store. Predict how many Fuj
to have available for the customers in December (month 12)?
Fuji Apples
5,325
5,648
5,873
9,842
8,234
9,421
10,123
9,784
10,443
9,564
10,147
Hint: When determining the solution to this question remember that am
up around holidays. Seasonality is a characteristic of a time series in wh
and predictable changes that recur every calendar year. Any predictable
series that recurs or repeats over a one-year period can be said to be se
Linear Model
12,000
y = 502.3364x 5568.1636
R² = 0.6998
10,000
Fuji Apples Purchased
Topic 5
8,000
6,000
4,000
2,000
0
2
4
6
8
10
Month
The Linear model is very slightly better, as R^2=69.98% is just a little higher than the R^2=69.35% for th
Visually, the two lines look very similar. The R^2 is a measure of model fit, as it indicates the amount o
This means that time in Months explains the variation in the number fo Fuji Apples Purchased in the lin
rio
ery store. Predict how many Fuji apples will need to be in stock
this question remember that amounts needed in a store will go
racteristic of a time series in which the data experiences regular
y calendar year. Any predictable change or pattern in a time
year period can be said to be seasonal.
Exponential Model
14,000
4x 5568.1636
y = 5613.7405e0.0659x
R² = 0.6935
Fuji Apples Purchased
12,000
10,000
8,000
6,000
4,000
2,000

10
12
0
2
4
6
Month
gher than the R^2=69.35% for the exponential model, which indicates a better model fit.
fit, as it indicates the amount of variation in the dependent, explained by the independent variable.
Fuji Apples Purchased in the linear regression model better than in the exponential.
8
10
5613.7405e0.0659x
10
12
Business Scenario
Scenario 5
Month
1
2
3
4
5
6
7
8
9
10
11
Predicting Fuji Apple Purchases
Review the monthly data involving Fuji apples purchased at a large grocery store. Predict how many Fuj
to have available for the customers in December (month 12)?
Fuji Apples
5,325
5,648
5,873
9,842
8,234
9,421
10,123
9,784
10,443
9,564
10,147
Hint: When determining the solution to this question remember that am
up around holidays. Seasonality is a characteristic of a time series in wh
and predictable changes that recur every calendar year. Any predictable
series that recurs or repeats over a one-year period can be said to be se
Linear Model
12,000
y = 502.3364x 5568.1636
R² = 0.6998
10,000
Fuji Apples Purchased
Topic 5
8,000
6,000
4,000
2,000
0
2
4
6
8
10
Month
The Linear model is very slightly better, as R^2=69.98% is just a little higher than the R^2=69.35% for th
Visually, the two lines look very similar. The R^2 is a measure of model fit, as it indicates the amount o
This means that time in Months explains the variation in the number fo Fuji Apples Purchased in the lin
rio
ery store. Predict how many Fuji apples will need to be in stock
this question remember that amounts needed in a store will go
racteristic of a time series in which the data experiences regular
y calendar year. Any predictable change or pattern in a time
year period can be said to be seasonal.
Exponential Model
14,000
4x 5568.1636
y = 5613.7405e0.0659x
R² = 0.6935
Fuji Apples Purchased
12,000
10,000
8,000
6,000
4,000
2,000

10
12
0
2
4
6
Month
gher than the R^2=69.35% for the exponential model, which indicates a better model fit.
fit, as it indicates the amount of variation in the dependent, explained by the independent variable.
Fuji Apples Purchased in the linear regression model better than in the exponential.
8
10
5613.7405e0.0659x
10
12

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