Welcome to Intro to Statistics: for Psychology and Business students! Business, data analysis, and psychology students all welcome!
Enroll today and find out why this course has 3 X more engagement compared to the average Math and Science course!
My statistics course is ideal for those studying on their own, or if you are in a statistics class and struggling with your assigned textbook or lecture material. I know stats courses can be boring, so I try to make it as exciting as possible.
The examples have a psychology bend, but this course is absolutely relevant for business students, especially those in data analysis that have to get a better understanding of statistical tests and the fundamental concepts. So welcome to you whether you are in business or psychology.
I provide examples within the lessons, so this should cut down your study time. Furthermore, I make sure that students understand the links between the different lessons. This helps you understand the bigger picture, and it also tends to decrease study time as you aren't stuck wondering "how does this all come together"?
Be able to distinguish between key statistical concepts and procedures
What's the difference between a sample and a population? Why do we test effect size after we get a significant result? What is a significant result? What's the difference between Type 1 and Type 2 errors? What role does probability play in bridging population and samples?
Choose the appropriate hypothesis test based on given information and make statistical inferences!
Calculate t-tests, ANOVA, correlation, linear regression... correctly and confidently
You will get clear instructions on how to calculate each of the tests. When you finish you will have the ability to calculate each test correctly and confidently.
Be able to break down each formula and explain its individual components
Understanding how to plug numbers and use formulas is important, but as important is to be able to understand what we are measuring. I'll break down the individual components of formulas/equations.
Contents and Overview
The course offers 3.5 hours or so of content. The 3.5 hours is sort of deceptive as I've tried to pack each second with something relevant and something important. I've made the course in a way to not waste your time and almost everything that is said in the lectures/lessons has a purpose.
What is statics? Also, how do you decipher between descriptive and inferential statistics? What role do each play? I'll show you a concrete example what role each of them play.
These four terms (population, sample, parameter, and statistic) are used so often that it's critical to understand what they are. You will not only be understand what each of them means, but you will see how concepts are all intertwined.
Discrete and continuous variables are difficult to understand if you aren't a researcher. I give you a process to follow to easily be able to decipher if something is a discrete or continuous variable. Definitions can be difficult to understand for these terms..my process on the other hand is easy to follow!
There are different levels of measurement in research. Higher levels of measurement (ie interval, ratio) allow us to take a mean and use certain statistical technique that we can't with nominal and ordinal data.
Understanding what type of measure of central tendency to describe your data is critical. The focus in this lesson is the mean, but we also consider the mode and median. The mean is used for a hypothesis testing so it is of particular interest to us.
The standard deviation measures how well the mean represents the data. Understand the importance of the standard deviation in hypothesis testing.
I answer 5 questions in this video related to variability (standard deviation).
1. Can variance or standard deviation be negative?
2. What does it mean when standard deviation is 0?
3. Why do we use n-1 and not n?
4. On an exam with a mean of 78, you obtain a score of X-84
a. would you prefer a standard deviation of 2 or 10?
b. if you scored x=72, would you prefer standard deviation of 2 or 10?
a. After 3 points have been added to every score in a sample, the mean is found to be M=83 and the standard deviation is s=8. What were the values for the mean and standard deviation for the original sample?
b. After every score in a sample has been multiplied by 4, the mean is found to be M=48 and the standard deviation is 12. What were the values for the mean and standard deviation for the original sample?
The normal distribution is the most important distribution in statistics. It describes a symmetric bell-shaped distribution. I explain when we utilize the normal distribution.
The purpose of z-scores, or standard scores, is to identify and describe the exact location of each score in a distribution. Find out how it is done.
I call this lesson "Probability made easy". I address what probability is in it's simplest form, I show you an example, and I explain how probability bridges/connects populations and samples.
The distribution of sample means is defined as the set of means from all the possible random samples of a specific size (n) selected from a specific population. I'll use a concrete example to show you what this definition means, and also explain why it's important to understand to understand sampling distribution of mean.
The standard error of the mean (SEM) (i.e., of using the sample mean as a method of estimating the population mean) is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population.
A hypothesis test is a statistical method that uses sample data to evaluate a hypothesis about a population.
These are not errors of calculation made by you, but just a result of the fact that we get to select what's called a significance level or alpha during the hypothesis testing process. When we select an alpha, we are in essence choosing what "bar" we want to set for ourselves in determining if we have a significant result. We can make it easier or harder to get a significant result, but there are ramifications for both, and this is what this lesson is about.
The t statistic is used to test hypotheses about an unknown population mean, μ,
when the value of population standard deviation is unknown. The formula for the t statistic has the same
structure as the z-score formula, except that the t statistic uses the estimated standard error in the denominator.
A t-test for independent samples, or t-test for a repeated measures design, uses a separate group of participants to represent each of the populations or treatment conditions being compared.
Do males have higher self esteem than females? I show you how to do an independent samples t-test, so you can find out.
Effect size is a measure of the magnitude or size of the treatment effect. It tells us about the separation between distributions. Since a significance test is impacted by sample size (the more sample, the more likely we are to have a significant result), effect size becomes really important.
Estimating an unknown population mean involves constructing a confidence interval. A confidence interval is based on the observation that a sample mean tends to provide a reasonably accurate estimate of the population mean. As a result, a confidence interval consists of an interval of values around a sample mean, and we can be reasonably confident that the unknown population mean is located somewhere in the interval.
For a t-test for repeated measures design,the same group of individuals is tested in both of the treatments..
Does being sleep deprived have a significant effect on problem solving? I help you answer this question by showing you how to perform a repeated samples t-test.
The t statistic is limited to situations that compare no more than two population means. Often, a research question involves the differences among more than two means and, in these situations, t tests are not appropriate.
ANOVA permits researchers to evaluate the mean differences among two or more
populations using sample data.
Is there a link between perfectionism and binge drinking? I show you an example of an independent measures ANOVA.
What is the effect of sleep deprivation on motor-skills performance? I show you an example of a repeated measures ANOVA.
A correlation measures the relationship between two variables, X and Y. The relationship is described by three characteristics: direction, form, strength.
Is there a correlation between a new 7-minute screen developed for Alzheimer's and the usual long cognitive-battery of tests? I show you an example of how correlation works, so you can find this answer for yourself!
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
Is the 7-minute screen for Alzheimer's (mentioned in the last example), predictive of what scores a patient would get by doing the longer set of tests? If so, how can this help us? I'll show you an example of linear regression?
Do children have color preferences? I show you an example of a chi-square goodness of fitness test..so you can find out for yourself.
How can using certain words, or asking a question in a certain way, impact eyewitness testimony? This might be crucial for forensic psychologists. I show you how to do a test of independence (chi-square), so you can find the answer to questions like this by yourself.
The success and fun I had with statistics based courses in University has resulted in my current teaching interests. My interest in data and statistics boils down to my passion for finding the objective truth, and applying these findings in life and business. Currently, I teach five courses. A Statistics course, a SAS course in English, a SAS course in Portuguese (with subtitles, but English instruction), a SAS SQL course, and a Pandas (Python 3 ) course.
O sucesso e diversão que eu tive durante meus cursos de estatística na Universidade resultaram em meu interesse em ensinar. Meu interesse em dados e estatística vêm de minha paixão por encontrar verdades objetivas, e aplicar estas descobertas na vida e no negócio. Atualmente, eu ensino quatro cursos. Um curso de Estatística, um curso de SAS em inglês, um curso de SAS em português (com legendas, mas instruções em inglês) e um curso de Pandas (Python 3).