Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Statistical Analysis I have all this data. Now what does it mean? Height (cm) Plant Height vs. Amount Watered 20 18 16 14 12 10 8 6 4 2 0 10 mL 25 mL 50 mL 1 Amount Watered Can you say that a bean plant will grow taller with 50 mL of water instead of 25 mL? Is your data Quantitative or Qualitative? Continuous quantitative – measurement scale divisible into partial units Ex-Distance in kilometers Discrete quantitative measurement scale with whole integers only Ex- number of wolves born in given year Qualitative Nominal - objects are named or can’t be ranked Example- Gender (male/female) Qualitative Ordinal - objects are placed into categories that can be ranked Example- activity of an animal on a scale of 1 to 5 Quantitative data can be subdivided into: Ratio - with equal divisible intervals & absolute zero Interval - does not have absolute zero Decide which type of data you have__________________ Describing data Central tendency (How different 2 sets of Data is) Mode - value that occurs most often Median - middle value when ranked highest to lowest Mean - mathematical average Variation (How spread out the data is) For quantitative data – Range, Standard Deviation σ , Variance σ2 http://www.mathsisfun.com/data/standard-deviation.html For qualitative data - Frequency distribution chi square by paul anderson Frequency Distribution Statistics Software … is not going to do your job for you. It is: not going to tell you what test to select not going to tell you if the test you selected is the right one not going to tell you how to interpret the test results. Making decisions about descriptive statistics & Graphs Quantitative Data Parameters Ratio data Type of data data collected using a scale with equal intervals and with an absolute zero (distance, velocity) Central tendency Mean Interval data using a scale with equal intervals but no absolute zero (temp0C, pH) Mean Qualitative Nominal data Ordinal data objects are placed into categories that cannot be ranked (male/female or brown, black, red hair) objects are placed into categories that can be ranked (Moh’s hardness scale or color ranked 1- 10) Mode Median Variation Range Standard deviation Variance Range Standard deviation Variance Frequency Distribution Frequency Distribution Degrees of freedom Total # of samples 2 (ex. 15+15-2 = 28) Total # of samples 2 (ex. 15+15-2 = 28) (#rows –1) x (#columns-1) (#rows –1) x (#columns-1) Level of significance 0.025 0.025 0.05 0.05 Decide which type of data you have, parameters you will need to calculate and on your Excel chart, enter the formula for each of the parameters. Inferential Statistics Is the data statistically significant? NOT due to random chance or error or uncontrolled variable Statistical Tests The t-test (or Analysis of Variance): two or more groups to compare measurements of each group. The Chi-square test: counts that can be placed into yes or no categories, or categories such as quadrants. The Pearson R Correlation: to test how the values of one event or object relates to the values of another event or object How to select statistical test? Is Dependent Variable (DV) continuous, ordinal, or nominal? Dependent Variable (DV) continuous Continuous IV T-test or ANOVA Nominal IV T-test or ANOVA Ordinal IV T-test Scatter plot Bar graph of means Bar graph of means Dependent Variable (DV) Ordinal Continuous IV Nominal IV Ordinal IV Chi-square Mann-Whitney’s test Spearman’s test Scatter plot Or Histogram Bar of means Scatter plot Dependent Variable (DV) Nominal Continuous IV Nominal IV T-test or F-test Paired-Mcnemar’s Unpaired-Chi-square Bar graph of means Bar graph of proportions Ordinal IV Spearman’s test Scatter plot Null Hypothesis (μ) …..states that there is NO difference between the mean of your control group and the mean of your experimental group. Therefore any observed difference between the two sample means occurred by chance and is not statistically significant. If you can reject your null hypothesis then there is a significant difference between your control and experimental groups. Hence accept the alternative (original hypothesis). Write your null hypothesis _____________________________ Probability - Chance Could the difference between the groups due to random chance /error? Probability of error or p-value < 0.05 means that the error in the research is 5/100 or below 0.05 (95% results have no error) P<0.05 Less than 5% chance is considered to be OK. Reject Null hypothesis Accept your alternative (original) hypothesis P>0.05 Greater than 5% then the data is not significant. Must accept Null hypothesis Level of significance () and Degree of freedom (df) Level of significance () - It communicates probability of error in rejecting Null hypothesis p-value < 0.05 means that the probability of error in the research is 5/100 (95% results with no error) Degree of freedom (df) - It is number of independent observations in a sample. t-test df = (n1-1) + (n2-1) Chi-square df = (#rows – 1) (#columns – 1) Pearson R correlation df = (n-2) subtract 2 from the number of comparisons made. T test Chi square tables.doc Accept or Reject the null hypothesis Find the table value for t-test and Chi-square test (using calculated degrees of freedom and Level of Significance of 0.05 = 95%) Compare calculated value to table value. Calculated value < table value means P>0.05 Null hypothesis is accepted Calculated value > or = table value means P<0.05 Null hypothesis is rejected. s/w does it for you, and will show you p-value If P<0.05 then reject Null & accept alternative hypothesis