Module 4 - Measures of Central Location

    That was a very fine day, the day when the first group of reporters will showcase their report. It was also the day that I was put in a great trouble, you know why? I was late that day, but I have a considerable reason to be late. Anyway, the reporters gave their best that day, they delivered their report pretty well considering the little time given for the preparations. They gave us a clear insight on the peculiarity of each measure of central tendency. They gave us also the specific uses of each measure including the hint on the most reliable measure to use. In summary, it was a very commendable report.

Module 5 - Measures of Variation

      It was our turn to report, together with my groupmates Sir Reden and Maam Karen. We were all ready to report that day, but sad to say I was not able to report due to the unavailability of time. I should be thankful for I still have much time to revise and improve my report, but then my agony of thinking how I may deliver my report well was extended for another week. 

    Our group was tasked to report on range, average deviation, quartile deviation, standard deviation, variance, and coefficient of variation. In God's mercy, we were all able to deliver it good and sufficiently for our classmates to comprehend. The names of these measures seems to suggest a difficult topic, but it's only a name because in reality, these are all easy to comprehend and to solve. That is, if you only focus well and practice more often. A copy of my report is being linked below.

Variance and Coefficient of Variation

Module 6 - Measures of Correlation

    In this topic, Dr. Ava made us understand the relationship between two given variables. We were made to solve problems and interpret the degree of relationship between the given variables. It was easy to solve but take extra careful in entering the numbers in your calculator, one false move and everything else fails. That's the wonder of correlation, if you commit mistake along the way, you might end up with an erroneous interpretation.

    On the other hand, Dr. Ava also taught us how to use the Microsoft Excel in solving for the correlation (can be found in her book Parametric Statistics Made Easy Using MS Excel, pages 62-64). The beauty of it is that we have the choice on what to use to solve for the correlation. This is another innovation introduced by Dr. Ava.

Module 7 - The Normal Probability Distribution

    Normal probability distribution? If there's a normal probability distribution, is there an abnormal probability distribution too? What a silly question, but that really crossed my mind. All these questions were given answers when Dr. Ava started to explain to us what normal probability distribution is. I have learned that normal probability distribution is considered the most prominent probability distribution in statistics. There are many reasons for this: First, the normal distribution is very tractable analytically, that is, a large number of results involving this distribution can be derived in explicit form. Second, the normal distribution arises as the outcome of the central limit theorem, which states that under mild conditions the sum of a large number of random variables is distributed approximately normally. Finally, the "bell" shape of the normal distribution makes it a convenient choice for modelling a large variety of random variables encountered in practice.

    Guess what.... There really exist an abnormal distribution. The term used for this is Skewness, a distribution can be positively or negatively skewed. Another term for not normal distribution is Kurtosis, a distribution that is more peaked than the normal is leptokurtic while one that is flatter than the normal is called platykurtic.

Module 8 - Sampling Theories and Hypothesis Testing

    Most of us find it hard to determine our sample population to be used in conducting our research. We hardly identity how many of the total population shall we take as our sample. Worry no more, for there is a very reliable way to solve for the sample size. This is what I have learned when Dr. Ava explained to us the topic on Sampling   Theories and hypothesis testing. We can use the Slovin's Formula in determining the sample size of the population.

    Dr. Ava also emphasized to us the proper way of testing the hypothesis. Before, I really don't understand what a null hypothesis is. I've heard a lot about it but I don't know what it is. Since Dr. Ava explained to us about it, I can now easily test hypothesis based on given data and situations. Reject or accept? Well, it depends on the result of the tcomputed and ttabled. If tcomputed is lesser than ttabled, then accept the null hypothesis. If the tcomputed is greater than the ttabled, then reject the null hypothesis.

Module 9 - The T-Test, Z-Test and Chi Square

     T-Test? Z-Test? Chi Square? What are those? Well, these are all inferential statistical tools. T-test is used to determine the significant difference between two means of independent samples that is less than 30. Z-test is used to determine the significant difference between the sample mean and the perceived population mean. The variables are more than 30. Chi square is an inferential statistical tool which determines the observed and expected frequencies of independent variables.

    In applying these tools, we used both the manual computation and the use of MS Excel. This made the learning more meaningful for we were able to compare the results from both methods.

Module 10 - Analysis of Variance (ANOVA)

    In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes t-test to more than two groups. Doing multiple two-sample t-tests would result in an increased chance of committing a type I error. For this reason, ANOVAs are useful in comparing two, three or more means.

    The reporters gave us a bird's eye view of the topic which we easily understood. They explained each step in a way that we could easily comprehend.