Happy new school year and welcome to our adventures in statistics! I am excited to work with you and uncover the ideas behind this fascinating subject. Together, we can create bridges over knowledge gaps to build an understanding of the goals and outcomes. So regardless of your background and relationship with math, open your mind to the wonderful world of statistics.
Print Copy of the Syllabus
This course presents the basic concepts underlying statistical methods and covers descriptive statistics, probability, probability distributions, hypothesis testing, estimates and sample sizes, correlation and regression, chi-square tests, analysis of variance, and nonparametric statistics. Technology is integrated into the course. Applications of statistics to business, life sciences, social sciences, psychology, and other areas are included.
By the end of this course, you will be able to…
Explain basic ideas and meanings of statistical terms such as: average, standard deviation, correlation, hypothesis tests, etc...
Present basic descriptive statistics: such as computing measures of central tendency and variation; and plotting basic graphs such as histograms and boxplots.
Identify and apply the basic laws of probability such as complements, independence, and the role of probability in statistics
Given an inferential statistics problem, identify the appropriate hypothesis test, perform the hypothesis test, and interpret the results.
Are you ready for an online class? Check here with the SMCCD Distance Education Gateway. Basically, if you have reliable access to the internet, are self-motivated, and can devote a good amount of time for this class, then can succeed in this class. Students reported spending between 5 to 30 hours per week on this course. Of course, it will be different for each person, so you will have to “find your groove” for this class.
Although this is an online course, the exams are proctored in order to keep the articulation agreements with transfer institutions. For this course, we will have two exam and a final that will have a proctored portion. With this set up, you can take the exam anywhere and anytime (before the deadline) with an internet connection and a computer with a web camera using Proctorio. If you have issues with this set up, you would need to email me and make other arrangements. More information will be posted before the first exam.
Learning Management System
We will be using Canvas. In addition to Canvas, we will use MyOpenMath for homework and other assignments.
Text
Our textbook will be the Introduction to Statistics, by Barbara Illowsky. It is available online through OpenStax.
Technology
Our main technology for this class will be Google Sheets. You are allowed to use Excel, the TI-84 or any other type of technology you may want to use.
Being a small college, Cañada resources can feel like an extended family. Starting with me, I hope you would feel comfortable to reach out to me for any questions regarding the classes or school. [Reminder: my email is rlapuz2@my.smccd.edu.] If I cannot answer the question, I will try to find resources to help. Here is a short list of resources that Cañada has to offer:
Check Yourself Quizzes (5%): These short open-book quizzes can be found right after the section videos along with a link to the text. The questions come directly from the textbook and are pooled, and you can take them as many times as you want; the program will take the highest score. These are due before the exam due dates, but it would be best to do them right after viewing the videos.
Homework (15%): Homework can be found in Canvas and will link to another site (MyOpenMath). These problems quickly assess your learning and understanding of the materials covered in the text. There are due dates for the assignments.
Make Up Policy: You can make up an assignment by sending me an email (rlapuz2@my.smccd.edu) with subject: “Math200 Make Up Homework” and the following information: which assignment(s) and your excuse for not completing them. The “absolute” deadline for an assignment is based on when it is covered on an exam: Chapters 1-4 cannot be made up after Exam #1; Chapters 6-9 cannot be made up after Exam #2; Chapters 10-13 will be closed by the Final Exam.
Journals (5%): Journal assignments generally tries to extend your study skills as a college student. There are assignments that have you discuss planning and reflecting. Some assignments encourage you to get to know some of your classmates through discussion board posts. Keep an eye out for possible extra credit opportunities in these journal assignments.
Example: Journal #2: Write a math autobiography and explore your struggles and success with math.
Two Midterms and the Final Exam (75%): Each of the exams will have two parts. The first part will be very similar to the homework assignments in MyOpenMath. The second will be the proctored portion and this will be from Canvas. Each exam group will be worth 25%. No Make Ups.
Have you been asked: "Are you good at math?" What was your response?
I'd like to propose that YOU ARE GOOD AT MATH!
I like to think about math as similar to playing a sport or performing for an audience. In either scenario, you can attain different levels of "being good." Some people may have great soccer skills like Lionel Messi, or may have a naturally amazing voice like Beyonce, but they did not get to their level without practice. Any by the way, they've also had their share of mistakes along the way: Messi and Beyonce, they they moved on and kept on learning.
So feel free to make your mistakes in the class. Just keep on learning and moving forward. YOU can do it!
https://www.youcubed.org/resources/mindset-video/
Michael Jordan Videos:
Mathematics comes from many cultures. Some of the ancient cultures and their contributions include Chinese (numerals), Egyptians (geometry), Greeks (mathematical thinking and proofs), and Mayans (the number ZERO).
There are also quite a bit of contemporary mathematicians from all walks of life who had significant contributions. Some notable mathematicians even made it to our big screens:
Chapter 1 Sampling and Data
1.1 Definitions of Statistics, Probability, and Key Terms
1.2 Data, Sampling, and Variation in Data and Sampling
1.3 Frequency, Frequency Tables, and Levels of Measurement
1.4 Experimental Design and Ethics
Chapter 2 Descriptive Statistics
2.1 Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs
2.2 Histograms, Frequency Polygons, and Time Series Graphs
2.3 Measures of the Location of the Data
2.4 Box Plots
2.5 Measures of the Center of the Data
2.6 Skewness and the Mean, Median, and Mode
2.7 Measures of the Spread of the Data
Chapter 3 Probability Topics
3.1 Terminology
3.2 Independent and Mutually Exclusive Events
3.3 Two Basic Rules of Probability
3.4 Contingency Tables
Chapter 4 Discrete Random Variables
4.1 Probability Distribution Function (PDF) for a Discrete Random Variable
4.2 Mean or Expected Value and Standard Deviation
4.3 Binomial Distribution
Chapter 5 Continuous Random Variables
5.1 Continuous Probability Functions
5.2 The Uniform Distribution
Chapter 6 The Normal Distribution
6.1 The Standard Normal Distribution
6.2 Using the Normal Distribution
Chapter 7 The Central Limit Theorem
7.1 The Central Limit Theorem for Sample Means (Averages)
7.2 The Central Limit Theorem for Sums
7.3 Using the Central Limit Theorem
Chapter 8 Confidence Intervals
8.1 A Single Population Mean using the Normal Distribution
8.2 A Single Population Mean using the Student t Distribution
8.3 A Population Proportion
Chapter 9 Hypothesis Testing with One Sample
9.1 Null and Alternative Hypotheses
9.2 Outcomes and the Type I and Type II Errors
9.3 Distribution Needed for Hypothesis Testing
9.4 Rare Events, the Sample, Decision and Conclusion
Chapter 10 Hypothesis Testing with Two Samples
10.1 Two Population Means with Unknown Standard Deviations
10.2 Two Population Means with Known Standard Deviations
10.3 Comparing Two Independent Population Proportions
10.4 Matched or Paired Samples
Chapter 11 The Chi-Square Distribution
11.1 Facts About the Chi-Square Distribution
11.2 Goodness-of-Fit Test
11.3 Test of Independence
Chapter 12 Linear Regression and Correlation
12.1 Linear Equations
12.2 Scatter Plots
12.3 The Regression Equation
12.4 Testing the Significance of the Correlation Coefficient
12.5 Prediction
Chapter 13 F Distribution and One-Way ANOVA
13.1 One-Way ANOVA