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Boston University Time Series in R Composition
This assignment provides practice in decomposing the seasonal time series data and then subtract that effect from the data ...
Boston University Time Series in R Composition
This assignment provides practice in decomposing the seasonal time series data and then subtract that effect from the data and then to show how to address issues of correlations between successive values of the time series. Before beginning this assignment, review the learning resources for this module, especially focus on Decomposing Time Series, Forecasting using Exponential Smoothing and ARIMA Models sections of A Little of R for Time Series by Avril Coghlan. Complete the following steps and write a report to record your work, results and analysis. You may use the attached data sets (average price data and NY benchmarked employment data) or one of time series data from data( ). Please note that some time series are more amenable to time series analysis than others.It is important that you understand data structure and be able to appropriately transform/clean raw data prior to analysis. "Average price data.xlsx" is in the long format. The data series are stacked on top of each other. That is how the data is provided the Bureau of Labor Statistics. The names of variables are provided in the "series" worksheet. You need to pick one series name and use it to filter or subset the data prior to analysis. Some series are better than others. You may want to use only the portions of the series after 2009, since the Great Recession was an one-time event that cannot be modeled.A. Decompose seasonal time series data and subtract that effect from the data:
Identify an appropriate time series data set, this can be a data set in R or a data set you find.
Then, use R to display decompose the seasonal time series and seasonally adjust to subtract the seasonal components from the time series.
In your report, provide insights to the results.
B. Address issues of correlations between successive values of the time series:
Identify an appropriate data set, this can be this can be a data set in R or a data set you find.
Then use R time series functions to address autocorrelation issues in data sets. In many cases you can make a better predictive model by taking correlations in the data into account. Address autocorrelation issues (irregular components of the time series) using a technique called ARIMA (Autoregressive Integrated Moving Average) models for irregular components of time series.
In your report, provide insights to the results.
Report Your assignment/project should have a good cover/title page, introduction of what the goals of the project and the methods you use. It also should follow APA format with at least 1000 words (excluding title page and references page) and references page. In the body of your project you should incorporate the R codes and R outputs with interpretation of your results. Finally, you need to make sense of your results to make good points with proper conclusions, to show your understanding of the course material and its application to the dataset. Graphs, figures, charts, tables are very useful to increase visual effects to impress your readers. You also should do your best to give insight and understanding to the project with a good conclusion. Please use subtitles to make your assignment more reader friendly as well.
ECON 120B FHS Econometrics Label Variable Missing Observations & Inwage Worksheet
Econometrics assignment using Stata please make sure to check the attach file beef accepting the bid.
ECON 120B FHS Econometrics Label Variable Missing Observations & Inwage Worksheet
Econometrics assignment using Stata please make sure to check the attach file beef accepting the bid.
22 pages
Mthh 040 059 Project 1
Be sure to include ALL pages of this project (including the directions and the assignment) when you send the project to yo ...
Mthh 040 059 Project 1
Be sure to include ALL pages of this project (including the directions and the assignment) when you send the project to your teacher for grading. ...
College of St Joseph Statistics Discussion Questions
Choose EITHER Option A or Option B for your initial post. Option A:An SRS of apartment listings in a large northeastern ci ...
College of St Joseph Statistics Discussion Questions
Choose EITHER Option A or Option B for your initial post. Option A:An SRS of apartment listings in a large northeastern city comparing monthly rent ($) versus size (〖ft〗^2) yields following computer output: check attached picture Is a linear model appropriate for these data?ExplainInterpret the slope of the regression line.〖^〗interpret r^2 in context.B:The calories and sugar content per serving size of ten brands of breakfast cereal are fitted with a least squares regression line with computer outputs: check attached picture.Is a linear model appropriate for these data?ExplainInterpret the slope of the regression line.〖^〗Interpret r^2 in context.
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using ...
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using
StatCrunch. We will work with a data set that has historical
importance in the development of the T-test.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Some features of this activity may not work well on a cell phone
or tablet. We highly recommend that you complete this activity on a
computer.Here are the directions, grading rubric, and definition of
high-quality feedback for the Learn by Doing
discussion board exercises.A list of StatCrunch directions is provided at the bottom of
this page.ContextGosset's Seed Plot DataWilliam S. Gosset was employed by the Guinness brewing company
of Dublin. Sample sizes available for experimentation in brewing
were necessarily small. At that time, Gosset contacted a famous
statistician Karl Pearson (1857-1936) and was told that there were
no techniques for developing probability models for small data
sets. Gosset studied under Pearson, and the outcome of his study
was perhaps the most famous paper in statistical literature, "The
Probable Error of a Mean" (1908), which introduced the
T-distribution.Since Gosset was employed by Guinness, any work he produced
would be owned by Guinness, so he published under a pseudonym,
"Student"; hence, the T-distribution is often referred to as
Student's T-distribution.To illustrate his analysis, Gosset used the results of seeding
11 different plots of land with two different types of seed:
regular and kiln-dried. He wanted to determine if drying seeds
before planting increased plant yield. Since different plots of
soil may be naturally more fertile, this confounding variable was
eliminated by using the matched pairs design and planting both
types of seed in all 11 plots.The resulting data (corn yield in pounds per acre) are as
follows.
Plot
Regular seed
Kiln-dried Seed
1
1903
2009
2
1935
1915
3
1910
2011
4
2496
2463
5
2108
2180
6
1961
1925
7
2060
2122
8
1444
1482
9
1612
1542
10
1316
1443
11
1511
1535
We use these data to test the hypothesis that kiln-dried seed
yields more corn than regular seed.Because of the nature of the experimental design (matched
pairs), we are testing the difference in yield.
Plot
Regular seed
Kiln-dried Seed
Difference
1
1903
2009
–106
2
1935
1915
20
3
1910
2011
–101
4
2496
2463
33
5
2108
2180
–72
6
1961
1925
36
7
2060
2122
–62
8
1444
1482
–38
9
1612
1542
70
10
1316
1443
–127
11
1511
1535
–24
Note that the differences were calculated:
regular −
kiln-dried.VariablesRegular seed: regular seeds that were traditionally
used for planting
kiln-dried: seed that were kiln-dried before plantingDataDownload the seed (Links to an external site.) data
file, and then upload the file into StatCrunch.PromptState the hypotheses and define the parameter.Checking conditions: Since Gosset invented the T-distribution,
we will assume that his sample meets the conditions and proceed
with the T-test. Regardless, answer these questions to demonstrate
your understanding of the conditions for use of the T-model.
But first you will need to review the dotplots for the data (opens
in a new tab).
Which graph is used to check conditions? Why?What do we look for in the graph to verify that conditions are
met?What else do we need to know about the sample of seeds before
using the T-test?
Use StatCrunch to find the T-score and the P-value. Hint: as
you work through the StatCrunch directions, keep in mind that we
want to calculate the differences as
regular −
kiln-dried . So you will choose
Regular seed for Sample 1 and kiln-dried seed for
Sample 2. (directions)
Copy and paste the information in the StatCrunch output window into
your initial post.State a conclusion based on the context of this scenario.EXAMPLE TO RIGHT ANSWER1. Ho: μ=0Ha: μ>0The average difference is -33.732. a) We use the graph of the differences because that is what
we are analyzing.b) We look to see if the graph is normally distributed, not
skewed, and doesn't have outliers.c) We don't know if the data is randomly selected.3.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Differences stored in column, Differences.4. Based on the P-value of 0.9391, we do not have enough
evidence to reject the null hypothesis. There is no statistically
significant evidence to show that kiln-dried seeds yield more than
regular seeds.
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Boston University Time Series in R Composition
This assignment provides practice in decomposing the seasonal time series data and then subtract that effect from the data ...
Boston University Time Series in R Composition
This assignment provides practice in decomposing the seasonal time series data and then subtract that effect from the data and then to show how to address issues of correlations between successive values of the time series. Before beginning this assignment, review the learning resources for this module, especially focus on Decomposing Time Series, Forecasting using Exponential Smoothing and ARIMA Models sections of A Little of R for Time Series by Avril Coghlan. Complete the following steps and write a report to record your work, results and analysis. You may use the attached data sets (average price data and NY benchmarked employment data) or one of time series data from data( ). Please note that some time series are more amenable to time series analysis than others.It is important that you understand data structure and be able to appropriately transform/clean raw data prior to analysis. "Average price data.xlsx" is in the long format. The data series are stacked on top of each other. That is how the data is provided the Bureau of Labor Statistics. The names of variables are provided in the "series" worksheet. You need to pick one series name and use it to filter or subset the data prior to analysis. Some series are better than others. You may want to use only the portions of the series after 2009, since the Great Recession was an one-time event that cannot be modeled.A. Decompose seasonal time series data and subtract that effect from the data:
Identify an appropriate time series data set, this can be a data set in R or a data set you find.
Then, use R to display decompose the seasonal time series and seasonally adjust to subtract the seasonal components from the time series.
In your report, provide insights to the results.
B. Address issues of correlations between successive values of the time series:
Identify an appropriate data set, this can be this can be a data set in R or a data set you find.
Then use R time series functions to address autocorrelation issues in data sets. In many cases you can make a better predictive model by taking correlations in the data into account. Address autocorrelation issues (irregular components of the time series) using a technique called ARIMA (Autoregressive Integrated Moving Average) models for irregular components of time series.
In your report, provide insights to the results.
Report Your assignment/project should have a good cover/title page, introduction of what the goals of the project and the methods you use. It also should follow APA format with at least 1000 words (excluding title page and references page) and references page. In the body of your project you should incorporate the R codes and R outputs with interpretation of your results. Finally, you need to make sense of your results to make good points with proper conclusions, to show your understanding of the course material and its application to the dataset. Graphs, figures, charts, tables are very useful to increase visual effects to impress your readers. You also should do your best to give insight and understanding to the project with a good conclusion. Please use subtitles to make your assignment more reader friendly as well.
ECON 120B FHS Econometrics Label Variable Missing Observations & Inwage Worksheet
Econometrics assignment using Stata please make sure to check the attach file beef accepting the bid.
ECON 120B FHS Econometrics Label Variable Missing Observations & Inwage Worksheet
Econometrics assignment using Stata please make sure to check the attach file beef accepting the bid.
22 pages
Mthh 040 059 Project 1
Be sure to include ALL pages of this project (including the directions and the assignment) when you send the project to yo ...
Mthh 040 059 Project 1
Be sure to include ALL pages of this project (including the directions and the assignment) when you send the project to your teacher for grading. ...
College of St Joseph Statistics Discussion Questions
Choose EITHER Option A or Option B for your initial post. Option A:An SRS of apartment listings in a large northeastern ci ...
College of St Joseph Statistics Discussion Questions
Choose EITHER Option A or Option B for your initial post. Option A:An SRS of apartment listings in a large northeastern city comparing monthly rent ($) versus size (〖ft〗^2) yields following computer output: check attached picture Is a linear model appropriate for these data?ExplainInterpret the slope of the regression line.〖^〗interpret r^2 in context.B:The calories and sugar content per serving size of ten brands of breakfast cereal are fitted with a least squares regression line with computer outputs: check attached picture.Is a linear model appropriate for these data?ExplainInterpret the slope of the regression line.〖^〗Interpret r^2 in context.
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using ...
Moore College of Art and Design Matched Pairs Statistics Worksheet
Learn by DoingMatched Pairs: In this lab you will learn how
to conduct a matched pairs T-test for a population mean using
StatCrunch. We will work with a data set that has historical
importance in the development of the T-test.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Some features of this activity may not work well on a cell phone
or tablet. We highly recommend that you complete this activity on a
computer.Here are the directions, grading rubric, and definition of
high-quality feedback for the Learn by Doing
discussion board exercises.A list of StatCrunch directions is provided at the bottom of
this page.ContextGosset's Seed Plot DataWilliam S. Gosset was employed by the Guinness brewing company
of Dublin. Sample sizes available for experimentation in brewing
were necessarily small. At that time, Gosset contacted a famous
statistician Karl Pearson (1857-1936) and was told that there were
no techniques for developing probability models for small data
sets. Gosset studied under Pearson, and the outcome of his study
was perhaps the most famous paper in statistical literature, "The
Probable Error of a Mean" (1908), which introduced the
T-distribution.Since Gosset was employed by Guinness, any work he produced
would be owned by Guinness, so he published under a pseudonym,
"Student"; hence, the T-distribution is often referred to as
Student's T-distribution.To illustrate his analysis, Gosset used the results of seeding
11 different plots of land with two different types of seed:
regular and kiln-dried. He wanted to determine if drying seeds
before planting increased plant yield. Since different plots of
soil may be naturally more fertile, this confounding variable was
eliminated by using the matched pairs design and planting both
types of seed in all 11 plots.The resulting data (corn yield in pounds per acre) are as
follows.
Plot
Regular seed
Kiln-dried Seed
1
1903
2009
2
1935
1915
3
1910
2011
4
2496
2463
5
2108
2180
6
1961
1925
7
2060
2122
8
1444
1482
9
1612
1542
10
1316
1443
11
1511
1535
We use these data to test the hypothesis that kiln-dried seed
yields more corn than regular seed.Because of the nature of the experimental design (matched
pairs), we are testing the difference in yield.
Plot
Regular seed
Kiln-dried Seed
Difference
1
1903
2009
–106
2
1935
1915
20
3
1910
2011
–101
4
2496
2463
33
5
2108
2180
–72
6
1961
1925
36
7
2060
2122
–62
8
1444
1482
–38
9
1612
1542
70
10
1316
1443
–127
11
1511
1535
–24
Note that the differences were calculated:
regular −
kiln-dried.VariablesRegular seed: regular seeds that were traditionally
used for planting
kiln-dried: seed that were kiln-dried before plantingDataDownload the seed (Links to an external site.) data
file, and then upload the file into StatCrunch.PromptState the hypotheses and define the parameter.Checking conditions: Since Gosset invented the T-distribution,
we will assume that his sample meets the conditions and proceed
with the T-test. Regardless, answer these questions to demonstrate
your understanding of the conditions for use of the T-model.
But first you will need to review the dotplots for the data (opens
in a new tab).
Which graph is used to check conditions? Why?What do we look for in the graph to verify that conditions are
met?What else do we need to know about the sample of seeds before
using the T-test?
Use StatCrunch to find the T-score and the P-value. Hint: as
you work through the StatCrunch directions, keep in mind that we
want to calculate the differences as
regular −
kiln-dried . So you will choose
Regular seed for Sample 1 and kiln-dried seed for
Sample 2. (directions)
Copy and paste the information in the StatCrunch output window into
your initial post.State a conclusion based on the context of this scenario.EXAMPLE TO RIGHT ANSWER1. Ho: μ=0Ha: μ>0The average difference is -33.732. a) We use the graph of the differences because that is what
we are analyzing.b) We look to see if the graph is normally distributed, not
skewed, and doesn't have outliers.c) We don't know if the data is randomly selected.3.Paired T hypothesis test:
μD = μ1 - μ2 : Mean of the
difference between Regular seed and Kiln-dried seed
H0 : μD = 0
HA : μD > 0
Hypothesis test results:
Difference
Mean
Std. Err.
DF
T-Stat
P-value
Regular seed - Kiln-dried seed
-33.727273
19.951346
10
-1.6904761
0.9391
Differences stored in column, Differences.4. Based on the P-value of 0.9391, we do not have enough
evidence to reject the null hypothesis. There is no statistically
significant evidence to show that kiln-dried seeds yield more than
regular seeds.
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