Definition of Terms

F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled.

 Chi-squared test, is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true, or any in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-squared distribution as closely as desired by making the sample size large enough.

Variance is a measure of how far a set of numbers is spread out. It is one of several descriptors of a probability distribution, describing how far the numbers lie from the mean (expected value). In particular, the variance is one of the moments of a distribution.

Critical value is the value corresponding to a given significance level. This cutoff value determines the boundary between those samples resulting in a test statistic that leads to rejecting the null hypothesis and those that lead to a decision not to reject the null hypothesis. If the calculated value from the statistical test is less than the critical value, then you fail to reject the null hypothesis. If the calculated statistic is outside of the critical value, then you reject the null hypothesis and are forced to accept the alternate hypothesis.

By: Lera Gay Bacay

Humor About Statistics

By: Patrick Anthony M. Alcantara

Hazing Facts

Here are some facts about statistics in hazing:

1. 1.5 million high school students are being hazed each year; 47% of students came to college already having experienced hazing.
2. 55% of college students involved in clubs, teams, and organizations experience hazing.
3. Alcohol consumption, humiliation, isolation, sleep-deprivation, and sexual acts are hazing practices common across all types of student groups.
4. 40% of athletes who reported being involved in hazing behaviors report that the coach or adviser was aware of the activity; 22% reported that the coach was involved.
5. As of the latest recorded hazing deaths in fraternities and sororities stands at 96-90 males and 6 females.
6. 82% of deaths from hazing involve alcohol.

By: Patrick Anthony M. Alcantara

Reflections

Hypothesis Testing

This second grading period, our lessons generally focuses on Hypothesis Testing.  Our (II-Gold) first encounter with Hypothesis Testing can be somewhat confusing, challenging and greatly needs our thinking and comprehensive ability, since there are many concepts being introduced at the same time.  to full understand all the detailed concepts, we must carefully follow each steps given by our teacher and do all the exercises assigned to us.  Only after careful study and patience will these concepts become clear.  and of course, it will be much clearer because of the teaching ability of Miss Kristin Macatigos.

z Test and t Test for a Mean

honestly speaking, this topic, for me, is the easiest compared to other topics that we've learned this grading period.  We've learned that if the sample size is 30 or more the test that should be used is the z test and if the sample size is less than 30, t Test will be used.  Although this topic is somewhat easy for the fact that almost all of us, the II-Gold students, have high scores regarding this but there are some problems that was given to us that were very tricky.  it thought me a lesson that in life we shouldn't too advantages to all things.

Testing the Difference Between Two Means, Two Variances and Two Proportions

After explaining all the basic concepts about Hypothesis Testing, we have stored knowledge about it that was really used when we discussed this chapter.
This chapter focuses in comparing two sample means, using experimental and control groups.  As a matter of fact, this chapter marks the start of our endless dilemmas an Advanced Statistics.  We were asking and saying "Okay? Ano kuno?" or even "Ha?! Paano nag amo run ka ra?!" and worse is "Yes Ma'am! 9faces his classmate) Ano kuno hambal ni Ma'am?" (Ma'am Macatigos, we're truly sorry about that.)  But nothing's impossible, before ending our lesson in this chapter we have gained knowledge that someday we wished we may able to share.

Correlation and Regression

This chapter is about determining whether a relationship between two or more numerical or quantitative variables exists.  This topic involves a lot of steps.  So you're very fortunate if your scientific calculator is a natural display since Ma'am Macatigos had taught us some shortcuts. 
If ever I am to describe the III-Gold students I would compare it to a damsel in distress.  If we are so careless then we have no choice but to go back from the start.  This topic really requires a lot of patience and being careful.  if there's a damsel in distress, there is always the knight in shining armor to the rescue.  And our knight in shining armor is Ma'am Macatigos (although she is a lady).  She never fails to rescue us in times of distress during our class in Advanced Statistics.

X² Test and Analysis of Variance

This chapter explains the chi-distribution and its applications especially in research.  This is also the last chapter of our lesson in Advanced Statistics.  This isn't so bad as we have all expected, it turns to be the opposite.  Yes, it was a hard lesson, and it was a great bliss that we have all passed this topic.    It was a great relief for all of us.  


By: Marie Louissie Ynez U. Lavega

Importance and Benefits of Statistics

Importance and Benefits of Statistics

1. Statistics provides simple yet instant information on the matter it centers on.

2. Statistical methods are useful tools in aiding researches and studies in different fieldssuch as economics, social sciences, business, medicine and many others.

3. Provides a vivid presentation of collected and organized data through the use of figures,charts, diagrams and graphs.

4. Helps provide more critical analyses of information






by: Oscar Pepin B. Marfil

In the table:

SSB = Between Group Sum-of-Squares

SSW = Within Group Sum-of-Squares

k = number of groups

N = n1 + n2 +...+ nk

MSW =    SS_W/(N-k)

MSB = SS_B/(k-1)

F = (S_B^2)/(S_W^2 )

SSt = 〖ΣX〗^2-〖ΣX〗^2/N

Where:

Σn(X_1-X_GM)     -       “sum of squares between groups”, denoted by SSB

Σ(n_1-1)S_1^2          -      “sum of squares between groups”,   denoted by SSB

If there is no difference from the means:

• the between-group variance estimate will be approximately equal to the within-group variance estimate 

• F Test value will be approximately equal to 1

• the null hypothesis will not be rejected

However, if the means differ significantly:

• the between-group variance will be much larger than the within-group variance

• F Test value will be significantly greater than 1

• the null hypothesis will be rejected

CORRELATION

Correlation is a statistical method used to determine whether a relationship between variables exists.

DIFFERENT TYPES OF CORRELATION

• Positive Correlation: If x and y have a strong positive linear correlation, r is close to ¬¬+1. An r value of exactly -1 indicates a perfect positive fit.
• Negative Correlation: If x and y have a strong negative linear correlation, r is close to -1. An r value of exactly -1 indicates a perfect negative fit.
• No Correlation: If there is no linear correlation or weak linear correlation, r is close to 0.
• Perfect Correlation: If  1 occurs only when the data points all lie exactly on a straight line. If r = +1, the slope of this line is positive. If r = -1, the slope of this line is negative.

A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally described as weak.