Intro to Statistics: for Psychology and Business students
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Intro to Statistics: for Psychology and Business students

​Understand how statistics are applied! Psychology and Business students welcome!
4.2 (18 ratings)
367 students enrolled
Created by Ermin Dedic
Last updated 11/2016
English
Current price: \$10 Original price: \$195 Discount: 95% off
30-Day Money-Back Guarantee
Includes:
• 3.5 hours on-demand video
• 7 Articles
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• Be able to distinguish between key statistical concepts and procedures
• You will be able to decipher the specific role of descriptive and inferential statistics
• You will be able to choose appropriate hypothesis test based on given information
• You will be able to calculate test statistics correctly and confidently
• You will be able to break down each formula and explain its individual components
View Curriculum
Requirements
• No textbook is necessary. Examples will be used.
• No prior experience with statistics is necessary. This is a beginner course.
Description

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!

• T-test (independent and repeated measures)
• ANOVA (independent and repeated measures)
• correlation (using the Pearson correlation)
• linear regression
• chi-square

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.

Enroll today!

Who is the target audience?
• This course is a good fit for those who feel anxious about anything math related and think of themselves as non-math people.
• This course is a good fit for those who like a direct style of teaching
• This course is ideal for those that like a quicker teaching style.
Curriculum For This Course
40 Lectures
03:24:32
+
Intro
1 Lecture 02:57
Preview 02:57
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Basic concepts and Descriptive Statistics
8 Lectures 41:15

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.

What is statistics + example what role descriptive and inferential stats play
04:05

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.

Population (Parameter) + Sample (Statistic)
03:20

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!

Discrete and Continuous Variables
05:00

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.

Measurement Scales
04:26

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.

Measures of Central Tendency
04:45

The standard deviation measures how well the mean represents the data. Understand the importance of the standard deviation in hypothesis testing.

Variability (Standard Deviation)
10:01

01:52

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?

5.

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?

Extra Content: Quick Questions and Quick Answers (Variability (s.d) Edition
07:46
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Reviews and ratings
1 Lecture 00:18
Leave a Review or Rating!
00:18
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Normal Distribution & Z-scores
4 Lectures 16:59
Frequency distributions and the Normal Distribution
01:16

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.

Normal Distribution
07:07

Unit normal table
00:08

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.

Z-scores (Probabilities, Percentiles, and Area between)
08:28
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Probability
2 Lectures 06:22

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.

Basics of Probability
05:57

Important takeaways from Probability lesson
00:25
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Probability and Samples
2 Lectures 21:02

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.

Sampling distribution of mean
12:20

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.

Standard error of mean
08:42
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Hypothesis Testing
2 Lectures 09:00

A hypothesis test is a statistical method that uses sample data to evaluate a hypothesis about a population.

Intro to Hypothesis Testing
06:07

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.

Type 1 and Type 2 error
02:53
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Tables (to find your critical region, or find proportions)
1 Lecture 00:10
All the tables you need for the course..including z-scores.
00:10
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Statistical Tests/Techniques
18 Lectures 01:46:21

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.

Intro to T-tests
03:18

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.

Independent samples t-test
04:59

Do males have higher self esteem than females? I show you how to do an independent samples t-test, so you can find out.

Preview 07:03

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.

Effect size - independent t-test
05:18

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.

Confidence Intervals (t-test independent samples example)
08:39

For a t-test for repeated measures design,the same group of individuals is tested in both of the treatments..

Repeated Measures t-test
05:14

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.

Repeated Measures Example - ​Does being sleep deprived have a significant effect
03:21

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.

Intro to ANOVA
06:34

Is there a link between perfectionism and binge drinking? I show you an example of an independent measures ANOVA.

Independent measure ANOVA example - ​Is there a link between perfectionism and b
09:49

What is the effect of sleep deprivation on motor-skills performance? I show you an example of a repeated measures ANOVA.

Repeated ANOVA example - ​What is the effect of sleep deprivation on motor skill
11:28

A correlation measures the relationship between two variables, X and Y. The relationship is described by three characteristics: direction, form, strength.

Correlation Intro
08:44

Hypothesis Testing for Correlation
02:01

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!

Correlation example- 7-minute screen for Alzheimer's and long cognitive battery
04:17

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.

Intro to Linear Regression
03:02

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?

Regression example-​7-min screen to predict scores for the long battery of tests
06:58

Chi-square intro
05:06

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.

chi-square Goodness of Fit example - do children have color preferences?
04:22

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.

test of independence example- how asking a question impacts eyewitness testimony
06:08
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Bonus (special offers)
1 Lecture 00:11
Bonus Lecture
00:11