Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.
this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.
this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
Week 3 Lecture 11
Regression Analysis
Regression analysis is the development of an equation that shows the impact of the
independent variables (the inputs we can generally control) on the output result. While the
mathematical language may sound strange, most of you are quite familiar with regression like
instructions and use them quite regularly.
To make a cake, we take 1 box mix, add 1¼ cups of water, ½ cup of oil, and 3 eggs. All
of this is combined and cooked. The recipe is an example of a regression equation. The output
(or result or dependent variable) is the cake, the inputs (or independent variables) are the inputs
used. Each input is accompanied by a coefficient (AKA weight or amount) that tells us how
“much” of the variable is “used” or weighted into the outcome.
So, in an equation format, this cake recipe might look like:
Y = 1X1 + 1.25X2 + .5X3 + 3X4 where:
Y = cake
X1 = box mix
X2 = cups of water
X3 = cups of oil
X4 = an egg.
Of course, for the cake, the recipe needs to go through the cooking process; while for
other regression equations the outputs need to go through whatever “process” turns the inputs
into the output – this is often called “life.”
Example
With a regression analysis, we can identify what factors influence an outcome. So, with
our Salary issue, the natural question to help us answer our research question of do males and
females get equal pay for equal work would be: what factors influence or explain an individual’s
pay? This is a perfect question for a multi-variate regression. Multi-variate simply means we have
multiple input variables with a single output variable (Lind, Marchel, & Wathen, 2008).
Variables. A regression analysis uses two distinct types of data. The first are variables
that are at least interval level or better (the same as the other techniques we have used so far).
The other is called a dummy variable, a variable that can be coded 0 or 1 indicating the presence
of some characteristic. In our data set, we have two variables that can be used as dummy coded
variables in a regression, Degree and Gender; both coded 0 or 1. In the case of Degree, the 0
stands for having a bachelor’s degree and the 1 stands for having an advanced degree. For
Gender, 0 means a male and 1 means a female. How these are interpreted in a regression output
will be discussed below. For now, the significance of dummy coding is that it allows us to
include nominal or ordinal data in our analysis.
Excel Approach. For our question of what factors influence pay, we will use Excel’s
Regression function found in the Data Analysis section. This function will produce two output
tables of interest. The first table tests to see if the entire regression equation is statistically
significant; that is, do the input variables significantly impact the output variable. If so, we
would then examine the second table – the coefficients used in a regression equation for e.
Assignment 2 Tests of SignificanceThroughout this assignmen.docxkarenahmanny4c
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies. You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables. You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted. You will complete all of the problems. Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
. You will need to submit the SPSS output file to get credit for this assignment. This file will save as a .spv file and will need to be in a single file. In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’. We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study. (Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable. Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET. Save the file in the following format: [your last name_SOCI332_A2]. The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
à
Compare Means
à
One-Sample T test
à
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
1.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance and at the .01 level (in SPSS this means you change the “confidence level” from 95% to 99%).
As part of Step 5, indicate whether the behavioral scientists should recommend group therapy for all depressed people based.
I need this done ASAP, You have to have SPSS Software on your comput.docxanthonybrooks84958
I need this done ASAP, You have to have SPSS Software on your computer. Please do not request to do the assignment if you don't have the software or if you do not have the understanding to get this assignment complete.
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies.
You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables.
You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted.
You will complete all of the problems.
Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
.
You will need to submit the SPSS output file to get credit for this assignment.
This file will save as a .spv file and will need to be in a single file.
In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison.
This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’.
We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study.
(Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable.
Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET.
Save the file in the following format: [your last name_SOCI332_A2].
The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
Compare Means
One-Sample T test
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance a.
Assignment 2 Tests of SignificanceThroughout this assignment yo.docxrock73
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies. You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables. You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how every question should be formatted. You will complete all of the problems. Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses. All calculations should be coming from your SPSS. You will need to submit the SPSS output file to get credit for this assignment. This file will save as a .spv file and will need to be in a single file. In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
1. State your research hypothesis (H1) and null hypothesis (H0).
2. Identify your confidence interval (.05 or .01)
3. Conduct your analysis using SPSS.
4. Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’. We will call this “p”.
5. Compare the two and apply the following rule:
a. If “p” is < or = confidence interval, than you reject the null.
Be sure to explain to the reader what this means in regards to your study. (Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable. Perhaps you bold your font or use a different color.
ASSIGNMENT 2(200) WORD MINIUM
1. They allow us to see if our relationship is "statistically significant". (Remember that this only shows us that there is or is not a relationship but does NOT show us if it is big, small, or in-between.)
2. It let's us know if our findings can be generalized to the population which our sample was selected from and represents.
This week you will decide which test of significance you will use for your project. For this class your choices for tests will include one of the following:
· Chi-square
· t Test
· ANOVA
We will be using a process for hypothesis testing which outlines five steps researchers can follow to complete this process:
1. Write your research hypothesis (H1) and your null hypothesis (H0).
2. Identify and record your confidence interval. These are usually .05 (95%) or .01 (99%).
3. Complete the test using SPSS.
4. Identify the number under Sig. (2-tail). This will be represented by "p".
5. Compare the numbers in steps 2 and 4 and apply the following rule:
1. If p < or = confidence interval, than you reject the null hypothesis
Determine what to do with your null and explain this to your reader. Be sure to go beyond the phrase "reject or fail to reject the null" and explain how that impacts your research and best describes the relationship between variables.
TEST QUESTIONS-NEED FULL ANSWERS
Q1
Make up and discuss research examples corresponding to the various ...
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. Data View
Data view represents the cases/data points for
different variables. Here we have one continuous
dependent variable, one continuous independent
variable and one categorical dependent variable.
3. Variable View
Variable view represents the properties of all
variables. Here group of categorical variable
are defined as:
1 = Male and 2 = female
4. Step 1 (Creating Dummy for Categorical
Variable)
Info: We are going to create dummy
for categorical variable. Here we have
2 subgroups so we need to create 1(2-
1) dummy variable.
Click on the Transform tab of
Menu bar then select
Recode into different
variables.
5. Step 2
Select the categorical
variable and drag it to
Numeric variable - >Output
variable box.
6. Step 3
Type new name and label for
dummy variable and click
change. Then click on the Old
and New variable button to
give the new values for
dummy.
7. Step 4
Info: We are creating dummy for Male
group (coded as 1) while Female group
(coded as 2) will be base.
Give old value for Male group
as 1 in the Old value box and
new value as 1 in New Value
box. Then click on add button
to add the preferred changes in
Old->New box.
8. Step 5
Select all other values (in this case
female) 2 in the Old value box and
new value as 0 in New Value box.
Then click on add button to add the
preferred changes in
Old->New box.
9. Step 6
Click on Ok button
to run the process
to create Dummy
variable.
10. Step 7
Here in the data view you
can see the new dummy
variable has been added
to the dataset.
11. Step 8 (Running Multiple Regression)
Running Linear Regression:
Click on the Analyze tab of
Menu bar then click
Regression option and select
Linear.
12. Step 9
Select Continuous dependent variable and
drag it to Dependent box and select
continuous and Categorical independent
variable and drag it to Independent(s) box.
13. Step 10
Click on Statistics button to
select desired statistics
option
14. Step 11
Select Estimates and
Confidence Interval under
Regression coefficients.
Select Durbin Watson under
Residual box and select
Model fit and Descriptive.
15. Step 12
Click on Ok button to get the
Output window with Results.
17. Correlation
This table displays the Correlation
between Dependent variable and
Independent variable with it’s associated
significance value..
18. Variable Information
This table displays the information about variables
in the model and variable that have been
removed from model in the Stepwise Method
19. Regression Model Summary
This table displays the R Square Value (Goodness
of Fit for the model) with it’s associated
significance value and Std. error of the estimates.
20. ANOVA Table
This table displays the Statistic for ANOVA with its associated
significance value that test the hypothesis that all of the
coefficients of Independent Variables are Zero.