The document discusses how to interpret the results of linear regression analysis. It explains that p-values indicate whether predictors significantly contribute to the model, with lower p-values meaning a predictor is meaningful. Coefficients represent the change in the response variable per unit change in a predictor. The constant/intercept is meaningless if all predictors cannot realistically be zero. Goodness-of-fit is assessed using residual plots and R-squared, though high R-squared does not guarantee a good fit. Interpretation focuses on statistically significant predictors and coefficients rather than fit metrics alone.
Explaining correlation, assumptions,coefficients of correlation, coefficient of determination, variate, partial correlation, assumption, order and hypothesis of partial correlation with example, checking significance and graphical representation of partial correlation.
Data Science - Part XII - Ridge Regression, LASSO, and Elastic NetsDerek Kane
This lecture provides an overview of some modern regression techniques including a discussion of the bias variance tradeoff for regression errors and the topic of shrinkage estimators. This leads into an overview of ridge regression, LASSO, and elastic nets. These topics will be discussed in detail and we will go through the calibration/diagnostics and then conclude with a practical example highlighting the techniques.
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Explaining correlation, assumptions,coefficients of correlation, coefficient of determination, variate, partial correlation, assumption, order and hypothesis of partial correlation with example, checking significance and graphical representation of partial correlation.
Data Science - Part XII - Ridge Regression, LASSO, and Elastic NetsDerek Kane
This lecture provides an overview of some modern regression techniques including a discussion of the bias variance tradeoff for regression errors and the topic of shrinkage estimators. This leads into an overview of ridge regression, LASSO, and elastic nets. These topics will be discussed in detail and we will go through the calibration/diagnostics and then conclude with a practical example highlighting the techniques.
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
FSE 200AdkinsPage 1 of 10Simple Linear Regression Corr.docxbudbarber38650
FSE 200
Adkins Page 1 of 10
Simple Linear Regression
Correlation only measures the strength and direction of the linear relationship between two quantitative variables. If the relationship is linear, then we would like to try to model that relationship with the equation of a line. We will use a regression line to describe the relationship between an explanatory variable and a response variable.
A regression line is a straight line that describes how a response variable y changes as an explanatory variable x changes. We often use a regression line to predict the value of y for a given value of x.
Ex. It has been suggested that there is a relationship between sleep deprivation of employees and the ability to complete simple tasks. To evaluate this hypothesis, 12 people were asked to solve simple tasks after having been without sleep for 15, 18, 21, and 24 hours. The sample data are shown below.
Subject
Hours without sleep, x
Tasks completed, y
1
15
13
2
15
9
3
15
15
4
18
8
5
18
12
6
18
10
7
21
5
8
21
8
9
21
7
10
24
3
11
24
5
12
24
4
Draw a scatterplot and describe the relationship. Lay a straight-edge on top of the plot and move it around until you find what you think might be a “line of best fit.” Then try to predict the number of tasks completed for someone having been without sleep 16 hours.
Was your line the same as that of the classmate sitting next to you? Probably not. We need a method that we can use to find the “best” regression line to use for prediction. The method we will use is called least-squares. No line will pass exactly through all the points in the scatterplot. When we use the line to predict a y for a given x value, if there is a data point with that same x value, we can compute the error (residual):
Our goal is going to be to make the vertical distances from the line as small as possible. The most commonly used method for doing this is the least-squares method.
The least-squares regression line of y on x is the line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible.
Equation of the Least-Squares Regression Line
· Least-Squares Regression Line:
· Slope of the Regression Line:
· Intercept of the Regression Line:
Generally, regression is performed using statistical software. Clearly, given the appropriate information, the above formulas are simple to use.
Once we have the regression line, how do we interpret it, and what can we do with it?
The slope of a regression line is the rate of change, that amount of change in when x increases by 1.
The intercept of the regression line is the value of when x = 0. It is statistically meaningful only when x can take on values that are close to zero.
To make a prediction, just substitute an x-value into the equation and find .
To plot the line on a scatterplot, just find a couple of points on the regression line, one near each end of the range of x in the data. Plot the points and connect them with a line. .
Requirements.docxRequirementsFont Times New RomanI NEED .docxheunice
Requirements.docx
Requirements:
Font: Times New Roman
I NEED 7 APA Style reference and In-text citation
Spacing: SINGLE
All the number of words are included next to the questions.
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BSBLDR511 - Develop and use emotional intelligence
Questions:
1. Explain emotional intelligence principles and strategies (100 words)
2. Describe the relationship between emotionally effective people and the attainment of business objectives (100 words)
3. Explain how to communicate with a diverse workforce which has varying cultural expressions of emotion (100 words)
4. List at least five (5) examples of emotional strengths and weaknesses. Explain all. (100 words)
5. Identify at least three (3) examples of emotional states you might identify in co-workers in the workplace, and outline the common cues for each. (100 words)
6. Why is it essential to consider varying cultural expressions of emotions when working and responding to emotional cues in a diverse workforce? (100 words)
7. There are a variety of opportunities you may provide in your workplace for others to express their thoughts and feelings. List two (2). ( 100 words)
8. Why is it important to assist others to understand the effect of their behavior and emotions on others in the workplace? ( 100 words)
9. What information will you need to consider to ensure you use the strengths of workgroup members to achieve workplace outcomes? (100 words)
Quiz 8 Notes
Scatterplots, Correlation and Regression
We are turning to our last quiz topic; regression. To get to regression, we need to understand several concepts first.
To start with, we will be working with two quantitative variables. The goal is to see if there is a relationship/association between the two variables. As one variable increases, what does the second variable do? If the second variable makes a consistent change then a relationship may exist. MAJOR POINT: saying a relationship exists does NOT mean there is Causation. The greatest abuse of statistical work is here, when a person runs a regression then says Variable A causes Variable B to change. You must have experimental results to establish causation.
Looking at the two variables that will be in a regression you need to know that each variable plays a specific role. One of the variables, X, will be the independent/explanatory variable and the other, Y, will be the dependent/ response variable. In a regression we are looking to see if changes in, Y; occur as X changes. It is very important that you establish at the beginning which of your variables will be X and which will be Y. Swapping the places for the two variables may not work. Let’s do an example.
In economics, we discuss the relationship of the quantity demand and the price of a good. Which one would be the X in a regression, and which would be, Y? The Law of Demand says, “as the price of a good increases, the quantity demanded decreases”. Which is allow.
ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings. Participants will be awarded Data Science international certification from UNIMAS.
Contains different types of Data Visualizations, best practices to follow for each case and what type of visualization should be made for different kinds of datasets.
Wireless charging using electromagnetic induction, and resonance magnetic coupling. Effects and limitations, cheallenges faced and meathods to overcome. Success Case study. References included.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Interpreting p-values
Regression analysis generates an equation to describe the statistical relationship between one or more predictor
variables and the response variable. Here you will learn about how to interpret the p-values and coefficients that
appear in the output for linear regression analysis.
The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (<
0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low p-value is likely to
be a meaningful addition to your model because changes in the predictor's value are related to changes in the
response variable.
Conversely, a larger (insignificant) p-value suggests that changes in the predictor are not associated with changes in
the response.
3. Interpreting p-values
In the output below, we can see that the predictor variables of South and North are significant because both of their
p-values are 0.000. However, the p-value for East (0.092) is greater than the common alpha level of 0.05, which
indicates that it is not statistically significant.
Typically, you use the coefficient p-values to determine which terms to keep in the regression model. In the model
above, we should consider removing East.
4. Interpreting Regression Coefficients
Regression coefficients represent the mean change in the response variable for one unit of change in the predictor
variable while holding other predictors in the model constant. This statistical control that regression provides is
important because it isolates the role of one variable from all of the others in the model.
The key to understanding the coefficients is to think of them as slopes, and they’re often called slope coefficients.
We will this in the fitted line plot below, where we will use a person’s height to model their weight.
5. Interpreting Regression Coefficients
The equation shows that the coefficient for height in meters is 106.5 kilograms. The coefficient indicates that for
every additional meter in height you can expect weight to increase by an average of 106.5 kilograms.
The blue fitted line graphically shows the same information. If you move left or right along the x-axis by an amount
that represents a one meter change in height, the fitted line rises or falls by 106.5 kilograms.
However, these heights are from middle-school aged girls and range from 1.3 m to 1.7 m. The relationship is only
valid within this data range, so we would not actually shift up or down the line by a full meter in this case.
If the fitted line was flat (a slope coefficient of zero), the expected value for weight would not change no matter how
far up and down the line you go. So, a low p-value suggests that the slope is not zero, which in turn suggests that
changes in the predictor variable are associated with changes in the response variable.
6. Interpreting the constant (Y intercept)
We have often seen the constant described as the mean response value when all predictor variables are set to zero.
Mathematically, that’s correct. However, a zero setting for all predictors in a model is often an
impossible/nonsensical combination, as it is in the following example.
In a previous example, we used a fitted line plot to illustrate a weight-by-height regression analysis. Below, I’ve
changed the scale of the y-axis on that fitted line plot, but the regression results are the same as before.
7. Interpreting the constant (Y intercept)
If you follow the blue fitted line down to where it intercepts the y-axis, it is a fairly negative value. From the
regression equation, we see that the intercept value is -114.3. If height is zero, the regression equation predicts that
weight is -114.3 kilograms!
Clearly this constant is meaningless and you shouldn’t even try to give it meaning. No human can have zero height
or a negative weight!
Now imagine a multiple regression analysis with many predictors. It becomes even more unlikely that ALL of the
predictors can realistically be set to zero.
8. Interpreting the constant (Y intercept)
Even if it’s possible for all of the predictor variables to equal zero, that data point might be outside the range of the
observed data.
You should never use a regression model to make a prediction for a point that is outside the range of your data
because the relationship between the variables might change. The value of the constant is a prediction for the
response value when all predictors equal zero. If you didn't collect data in this all-zero range, you can't trust the
value of the constant.
9. Interpreting the constant (Y intercept)
The height-by-weight example illustrates this concept. These data are from middle school girls and we can’t
estimate the relationship between the variables outside of the observed weight and height range. However, we can
get a sense that the relationship changes by marking the average weight and height for a newborn baby on the
graph. That’s not quite zero height, but it's as close as we can get.
10. Interpreting the constant (Y intercept)
There is a red circle near the origin to approximate the newborn's average height and weight. You can clearly see
that the relationship must change as you extend the data range!
So the relationship we see for the observed data is locally linear, but it changes beyond that. That’s why you
shouldn’t predict outside the range of your data...and another reason why the regression constant can be
meaningless.
11. Goodness of fit
After you have fit a linear model using regression analysis, you need to determine how well the model fits the data.
What is Goodness of fit of a Linear Model?
Linear regression calculates an equation that minimizes the distance between the fitted line and all of the data
points. Technically, ordinary least squares (OLS) regression minimizes the sum of the squared residuals.
In general, a model fits the data well if the differences between the observed values and the model's predicted
values are small and unbiased.
Before you look at the statistical measures for goodness-of-fit, we will look at checking the residual plots.
Residual plots can reveal unwanted residual patterns that indicate biased results more effectively than numbers.
When your residual plots pass muster, you can trust your numerical results and check the goodness-of-fit statistics.
12. Residual Plots
To start, let’s breakdown and define the 2 basic components of a valid regression model:
Response = (Constant + Predictors) + Error
Another way we can say this is:
Response = Deterministic + Stochastic
The Deterministic Portion –
This is the part that is explained by the predictor variables in the model. The expected value of the response is a
function of a set of predictor variables. All of the explanatory/predictive information of the model should be in this
portion.
The Stochastic Error –
Stochastic is a fancy word that means random and unpredictable. Error is the difference between the expected
value and the observed value. Putting this together, the differences between the expected and observed values
must be unpredictable. In other words, none of the explanatory/predictive information should be in the error.
The idea is that the deterministic portion of your model is so good at explaining (or predicting) the response that
only the inherent randomness of any real-world phenomenon remains leftover for the error portion. If you observe
explanatory or predictive power in the error, you know that your predictors are missing some of the predictive
information.
Residual plots help you check this!
13. Using Residual Plots
Using residual plots, you can assess whether the observed error (residuals) is consistent with stochastic error.
This process is easy to understand with a die-rolling analogy. When you roll a die, you shouldn’t be able to predict
which number will show on any given toss. However, you can assess a series of tosses to determine whether the
displayed numbers follow a random pattern. If the number six shows up more frequently than randomness dictates,
you know something is wrong with your understanding (mental model) of how the die actually behaves.
The same principle applies to regression models. You shouldn’t be able to predict the error for any given
observation. And, for a series of observations, you can determine whether the residuals are consistent with random
error. Just like with the die, if the residuals suggest that your model is systematically incorrect, you have an
opportunity to improve the model.
14. Using Residual Plots
The residuals should not be either systematically high or low. So, the residuals should be centered on zero
throughout the range of fitted values.
In other words, the model is correct on average for all fitted values.
Further, in the OLS context, random errors are assumed to produce residuals that are normally distributed.
Therefore, the residuals should fall in a symmetrical pattern and have a constant spread throughout the range.
Here's how residuals should look:
15. Using Residual Plots
Now let’s look at a problematic residual plot. Keep in mind that the residuals should not contain any predictive
information.
In the graph above, you can predict non-zero values for the residuals based on the fitted value. For example, a
fitted value of 8 has an expected residual that is negative. Conversely, a fitted value of 5 or 11 has an expected
residual that is positive.
16. Using Residual Plots
The non-random pattern in the residuals indicates that the deterministic portion (predictor variables) of the model
is not capturing some explanatory information that is “leaking” into the residuals.
The graph could represent several ways in which the model is not explaining all that is possible. Possibilities include:
• A missing variable
• A missing higher-order term of a variable in the model to explain the curvature
• A missing interaction between terms already in the model
In addition to the above, here are two more specific ways that predictive information can sneak into the residuals:
• The residuals should not be correlated with another variable. If you can predict the residuals with another
variable, that variable should be included in the model.
• Adjacent residuals should not be correlated with each other (autocorrelation). If you can use one residual to
predict the next residual, there is some predictive information present that is not captured by the predictors.
Typically, this situation involves time-ordered observations.
For example, if a residual is more likely to be followed by another residual that has the same sign, adjacent
residuals are positively correlated.
17. R-Squared
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the
coefficient of determination, or the coefficient of multiple determination for multiple regression.
The definition of R-squared is fairly straight-forward; it is the percentage of the response variable variation that is
explained by a linear model.
R-squared is always between 0 and 100%:
• 0% indicates that the model explains none of the variability of the response data around its mean.
• 100% indicates that the model explains all the variability of the response data around its mean.
In general, the higher the R-squared, the better the model fits your data.
18. Graphical Representation of R-Squared
Plotting fitted values by observed values graphically illustrates different R-squared values for regression models.
The regression model on the left accounts for 38.0% of the variance while the one on the right accounts for 87.4%.
The more variance that is accounted for by the regression model the closer the data points will fall to the fitted
regression line.
Theoretically, if a model could explain 100% of the variance, the fitted values would always equal the observed
values and, therefore, all the data points would fall on the fitted regression line.
19. Limitations of R-Squared
1. R-squared cannot determine whether the coefficient estimates and predictions are biased, which is why you
must assess the residual plots.
2. R-squared does not indicate whether a regression model is adequate. You can have a low R-squared value for a
good model, or a high R-squared value for a model that does not fit the data!
20. Are low R-Squared values bad?
No! There are two major reasons why it can be just fine to have low R-squared values.
In some fields, it is entirely expected that your R-squared values will be low. We get a R-squared value of 37~40 % in
finance and we are happy.
Furthermore, if your R-squared value is low but you have statistically significant predictors, you can still draw
important conclusions about how changes in the predictor values are associated with changes in the response
value.
Regardless of the R-squared, the significant coefficients still represent the mean change in the response for one unit
of change in the predictor while holding other predictors in the model constant. Obviously, this type of information
can be extremely valuable.
A low R-squared is most problematic when you want to produce predictions that are reasonably precise (have a
small enough prediction interval).
21. Are high R-Squared values good?
No! A high R-squared does not necessarily indicate that the model has a good fit. That might be a surprise, but look
at the fitted line plot and residual plot below. The fitted line plot displays the relationship between semiconductor
electron mobility and the natural log of the density for real experimental data.
22. Are high R-Squared values good?
The fitted line plot shows that these data follow a nice tight function and the R-squared is 98.5%, which sounds
great. However, look closer to see how the regression line systematically over and under-predicts the data (bias) at
different points along the curve. You can also see patterns in the Residuals versus Fits plot, rather than the
randomness that you want to see. This indicates a bad fit, and serves as a reminder as to why you should always
check the residual plots.
23. Conclusion on R-Squared
Similar biases can occur when your linear model is missing important predictors, polynomial terms, and interaction
terms.
R-squared is a handy, seemingly intuitive measure of how well your linear model fits a set of observations.
However, as we saw, R-squared doesn’t tell us the entire story. You should evaluate R-squared values in conjunction
with residual plots.
24. Interpretation on Standard Error of Regression
Similar biases can occur when your linear model is missing important predictors, polynomial terms, and interaction
terms.
R-squared is a handy, seemingly intuitive measure of how well your linear model fits a set of observations.
However, as we saw, R-squared doesn’t tell us the entire story. You should evaluate R-squared values in conjunction
with residual plots.