STATSMAG
OCTOBER 2021
LEARN NEW MORE TEST EXERCISE YOUR MIND
FINDING THE PERFECT TEST FOR YOUR DATA
AUTUMN TRENDS
FIND OUT STATISTIC TRENDS
HAVE YOU HEARD THE
NEWS??
STATSMAG IS BACK FOR VOLUME 2 AND WE ARE SO EXCITED TO SHARE OUR KNOWLEDE AGAIN ABOUT STATISTICS
TABLE OF CONTENTS
T - Test
- Definition
- Writing of Hypothesis - Two types of T - Test Correlation
- Definition
- Writing of Hypothesis
- Two types of Correlation Chi Square
- Definition
-Writing of Hypothesis
- Two Types of Chi Square Editor's Page
T - TEST Looks at whether the t score of category A's mean is within or outside the expected range described by category B's normalized curve.
T - TEST There are ways in writing our hypothesis for T - Test. Let's check it out!!
NULL HYPOTHESIS Ho : There is NO SIGNIFICANT DIFFERENCE in variable W (category 1 of variable X) and (category 2 of variable X)
ALTERNATIVE HYPOTHESIS HA : There is A SIGNIFICANT DIFFERENCE in variable W (category 1 of variable X) and (category 2 of variable X)
T - TEST Since we already know how to write the null and alternative hypothesis for T - test, let's know look at the two types of it!!
PAIRED SAMPLE T- TEST Paired Sample T - Test compares the normalized t - distribution of the same group. Our analysis has to match the values of the same person. It may sound difficult but we assure you that it is very easy to understand it!!
PAIRED SAMPLE T- TEST there are two steps for this type of t - test.
STEP 1: IS OUR P - VALUE SIGNIFICANT? STEP 2 : DO THE CONFIDENCE INTERVALS CONTAIN THE VALUE OF "O.OO"?
PAIRED SAMPLE T- TEST for the step 2 of paired sample t- test
if the interval contains the "0.00", then the two normal curves are overlapping.
PAIRED SAMPLE T- TEST how to write in paper : " the statistics t(df) = (statistics), is significant at p - value <0.01. thus we can reject the null hypothesis. the upper and lower confidence intervals also do not cross 0, iNDICATING A SIGNIFANCE DIFFERENCE..."
INDEPENDENT SAMPLE T - TEST for our 2nd type of t - test, independent sample t - test compares the normalized t distribution of two groups and just like paired sample t - test, it requires normal data. this type of t- test is easy to understand!!
INDEPENDENT SAMPLE T - TEST this 2nd type is also just like paired sample t - test. it also have 2 steps that is just the same with the first type of t - test.
INDEPENDENT SAMPLE T - TEST how to write in paper : " the statistics t(df) = (statistics), is significant at p - value <0.05. the upper and lower confidence intervals also do not cross 0, iNDICATING A SIGNIFANCE DIFFERENCE..."
p value less than significance level (0.05) : there is significant difference
p value greater than significance level (0.05) : there is no significant difference
CORRELATION
It is how two variables are related to one another or move in relation to another.
NULL HYPOTHESIS Ho : There is NO SIGNIFICANT RELATIONSHIP between variable W and variable X
ALTERNATIVE HYPOTHESIS HA : There is A SIGNIFICANT RELATIONSHIP between variable W and variable X
CORRELATION
LET US KNOW THE TWO DIFFERENT TYPES OF CORRELATION
PEARSON'S R THIS TYPE of correlation IS MEANT FOR DATA THAT IS NORMAL. IN STAT SPEAK,IT LOOKS AT THE LINEAR RELATIONSHIP BETWEEN THE RAW DATA
SPEARMAN'S R THIS TYPE IS FOR NOT NORMAL DATA. IT IS BASED ON THE RANKED VALUES FOR EACH VARIABLE RATHER THAN THE RAW DATA. WE CAN SEE CLEARLY THE DIFFERENCE IN THE TWO TYPES OF CORRELATION.
CORRELATION
LET US KNOW THE DIFFERENT CORRELATION COEFFICIENT
CORRELATION COEFFICIENT 1.0 : Perfect Positive
0.6 to 0.99 : Strong Positive
0.3 to 0.59 : Moderate Positive
0.1 to 0.29 : Weak Positive
-0.1 to 0.1 : No correlation
-0.1 to -0.29 : Weak Negative
-0.3 to -0.59 : Moderate Negative
-0.6 to -0.99 : Strong Negative
-1.0 : Perfect Negative
CORRELATION there are three steps for correlation
STEP 1: the p value. if our p value is less than 0.05 then the two data has a correlation and we reject the null hypothesis. we don't proceed to step 2 if our data is not significant.
CORRELATION there are three steps for correlation
STEP 2 : we look at the coefficient. we check if what type of correlation coefficient is seen in our data.
CORRELATION there are three steps for correlation
step 3 : the write up. we write our discussion as "pearson's r correlation statistics show a (type of correlation coefficient) between (two variables), significant at p value <0.05"
CHI - SQUARE
It compares the observed vs expected frequencies of discrete variable. It does not require for normality.
NULL HYPOTHESIS Ho : There is NO SIGNIFICANT DIFFERENCE in W across different categories of X. (independent, same)
ALTERNATIVE HYPOTHESIS HA : There is A SIGNIFICANT DIFFERENCE in W across different categories of X. (not independent, affected)
CHI - SQUARE
LET US SEE THE TWO DIFFERENT TYPES OF CHI SQUARE
GOODNESS OF FIT IT ONLY USES DISCRETE VARIABLE AND THE EXPECTED VALUES GIVEN BY AT THE START IN A RATIO. THIS TYPE IS SIMPLER THAN THE 2ND TYPE OF CHI SQUARE.
INDEPENDENCE IT USES TWO DISCRETE VARIABLES AND THE EXPECTED VALUES ARE CALCULATED. THIS TYPE IS MORE COMPLEX. THIS TYPE IS MORE RELEVANT IN A PSYCH RESEARCH
did you learn a
lot??
STATSMAG will be back for volume 3 and we are hoping you are still here for us!!
Editor's Page Get to know more about the Editor - in - Chief of STATSMAG
I am a 2nd year college student who is pursuing Bs - Psychology as my pre - med course. Someday I want to be a Pediatrician because of my love for kids. My friends call me the 'mother' of the group because I always care for them and help them if there is a chance. My friends also call me workaholic because I never know when to stop doing academics and org works. I can't deny that I'm a workaholic because it is really true. Despite being a workaholic, I still find time to rest and to catch up with my personal life. My love for Kpop makes me sane and makes me happy. Watching Kpop contents or just listening to their music makes me relax. The groups that I do love are EXO, WayV and NCT. So the next time you see me or ask me what makes me happy, I'll only answer you "Kpop" or "Kpop merchs".
MARTHA ANGELA S. AMON Editor-in-Chief
STATSMAG
Editor's Note If you're already at this page, I congratulate you for finishing my magazine. I hope you enjoyed my magazine! I hope you also learned a lot from it. I may not be a professional when it comes to Statistics but I do really hope that somehow this magazine helped you even in the smallest way. Thank you for your endless support for me and my magazine! I learned a lot from making this magazine and I dedicated my time in finishing this so Thank you so much! I hope to see you again in my next magazine!! Have a nice day and stay safe always!!