Statistics for Artificial intelligence and data science
Statistics for Artificial intelligence and data science
- short note on ARP RARP? ARP (Address Resolution Protocol) finds the MAC (physical) address for a known IP address; RARP (Reverse ARP) finds the IP address for a kno... 1
- short note on GO-Back-N ARQ protocol Go-Back-N ARQ is a sliding-window error-control protocol in which the sender may send up to N frames without waiting for acknowledgements; i... 1
- Define ‘t’ test. Explain the different situations where paired and unpaired ‘t’ tests applied The t-test is a small-sample test (population SD unknown) that checks whether a mean, or the difference between two means, is statistically... 1
- Discuss- Histogram, Bar diagram. A histogram represents a continuous frequency distribution with touching bars; a bar diagram represents discrete or categorical data with se... 1
- Briefly describe the different distribution patterns of data. Data can be distributed in different shapes: symmetric (normal), positively skewed, negatively skewed, uniform, bimodal/multimodal and J-sha... 1
- Explain one way ANOVA and the assumptions in one way ANOVA. One-way ANOVA compares the means of three or more groups that differ in one factor, using F = MSB/MSW; it assumes independence, normality an... 1
- Explain chi square test for Goodness of fit. The chi-square goodness-of-fit test checks whether observed frequencies agree with the frequencies expected from a theoretical distribution,... 1
- Explain with examples- Histogram, Pie chart. A histogram shows the frequency distribution of continuous (numerical) data using adjacent bars; a pie chart shows how a whole is divided in... 1
- Explain ANOVA and its significance. ANOVA (Analysis of Variance) tests whether three or more population means are equal by splitting the total variation into variation between... 1
- Explain student‘t’ test and its applications. Student's t-test is a test of significance for means when the sample is small (usually n < 30) and the population standard deviation is unkn... 1
- Describe the various of graphical methods of representing quantitative data. The main graphical methods for quantitative data are the histogram, frequency polygon, frequency curve, ogive, stem-and-leaf plot, box plot,... 1
- Explain the hypothesis testing using one way of ANOVA. One-way ANOVA tests H₀: μ₁ = μ₂ = dots = μ_k by comparing the variance between group means with the variance within groups through F = MSB/M... 1
- Explain probability and its significance in statistical analysis. Probability is the measure of how likely an event is, expressed from 0 (impossible) to 1 (certain); it is the foundation of statistical anal... 1
- Discuss with examples measures of central tendency. Measures of central tendency give a single value that represents the centre of the data; the three main ones are the mean, median and mode.... 1
- Explain null hypothesis, type I and type II errors. The null hypothesis ( H₀) is the statement of "no effect / no difference" that a test tries to reject; a Type I error is rejecting a true H₀... 1
- Explain correlation, types of correlation and its applications. Correlation is the statistical relationship between two variables, showing whether and how strongly they move together; it is measured by th... 1
- Explain the graphical methods of representing quantitative data. Quantitative data is shown graphically mainly by histograms, frequency polygons, frequency curves, ogives (cumulative frequency curves), ste... 1
- Discuss different methods of sampling. Sampling methods are of two kinds: probability sampling (every unit has a known chance of selection) and non-probability sampling (selection... 1
- Explain ANOVA and its applications. ANOVA (Analysis of Variance) is a test that checks whether the means of three or more groups are equal, by comparing the variation between g... 1
- What are measures of dispersion? Explain. Measures of dispersion tell how spread out the data values are around a central value; the main ones are range, quartile deviation, mean dev... 1
- Define probability and explain its significance in statistical inference with examples. Probability is a number between 0 and 1 that measures how likely an event is; in statistical inference it is the tool that lets us draw conc... 1
- Explain types of correlation and correlation coefficient. Give suitable examples. Correlation measures how two variables change together; its strength and direction are given by the correlation coefficient r, which lies be... 1