Data Management PDF

DATA MANAGEMENT B. Inferential Statistics Statistics Methods concerned with drawing conclusions, making predictions, or deals with the scientific collection, forecasts, ab...

DATA MANAGEMENT B. Inferential Statistics Statistics Methods concerned with drawing conclusions, making predictions, or deals with the scientific collection, forecasts, about the entire set of data. organization, presentation, analysis and interpretation of data to obtain useful and Examples: meaningful information. A manager of a business firm predicts Data future sales of the company based on the present sales. Any piece of information useful to the A psychologist investigates if there is researcher; the measurements obtained in a a significant relationship between the research study. mental age and chronological age. Individuals Mean - average Are the people, places, events, or objects Median - middle value included in the study. Mode - values that most appears Variable Range - highest to lowest value Is a characteristic or property of an individual to be measured or observed. Standard Deviation- spread the scores from the mean a. Quantitative Variable Variance (s2)- square of standard deviation Has a value or numerical measurement Level of Significance (a): maximum - Discrete Variable – is a variable that probability can be obtained by counting. - Continuous Variable – is a variable that can be obtained by measuring. USES OF CHART/GRAPHS: b. Qualitative Variable A variable that categorizes or describes an element of a population. A. Descriptive Statistics Methods concerned with collecting, describing (organizing, presenting, summarizing, and analyzing) a set of data LEVEL OF MEAUREMENT AND SCALE: without drawing conclusions or inferences about a large group. Examples: A teacher computes the average grade of his students and then determines the top 10 students. A janitor counts the number of various furniture inside the school Justine Lansangan  Variable - characteristic assume different TYPE OF ERROR values Type I Error: reject the null when its true Data - measurements or observations Population - all subjects that are being Type II Error: failed to reject the null when its studied false Sample - group that selected from a population Parameter - taken from a population DIRECTIONAL OF STATISTICAL TEST Statistic - taken from a sample One-tailed test (Directional test): one only is being observed. Eg. less or greater than TYPE OF PROBABILITY: Two-tailed test (non-directional test): two or more are being observed. Eg. not equal Probability - utilizes random sampling techniques to create a sample. This group of sampling methods gives all the members of a population equal chances of being selected. DECISION MAKING: Non-Probability - a group of sampling techniques where the samples are collected in a way that does not give all the units in the population equal chances of being selected. SAMPLING TECHNIQUES: CORRELATION: Random Sampling - have a chance to be Correlation coefficient: determine the selected strength of the linear relationship between Systematic Sampling - selecting by two variables pattern in alphabetical. Eg: (12th ) Positive Correlation - same direction Stratified Sampling - dividing the Negative Correlation - different population into subgroups (strata) direction according to some characteristics, then No Correlation - no direction samples are proportionally selected from each subgroup Cluster Sampling - By dividing the VARIABLES: population into sections (clusters) and then selecting one or more clusters, using all members in the cluster/s HYPOTHESIS Hypothesis Testing - decision making Null Hypothesis (Ho) - absence of relationship Alternative Hypothesis (Ha) - presence of relationship Justine Lansangan COMMONLY USED STATISTICAL TEST: APPROACHES TO PROBLEM SOLVING: T-test – difference between two A. Deductive Reasoning groups ANOVA (Analysis of variance) – It is the process of reasoning to a specific difference involving more than 2 conclusion from general statement. groups Examples: Linear regression – making prediction for continuous or numeric A quadrilateral has four sides. A data square has four sides. Therefore, a square is a quadrilateral. B. Inductive Reasoning PROBLEM SOLVING In inductive reasoning, one generalizes Problem – a task that requires the learner to based on individual instances or specific reason through a situation observations. Exercise – provides practice in using Often used by mathematicians and algorithms and maintaining the basic facts. scientists to predict answers to complicated Problem Solving – encompasses exploring, problems. reasoning, strategizing, estimating, conjecturing, testing, explaining, and proving. Examples: Exercise/Drill – An exercise/drill is a set of What is the next term? 1,3,5,7,9,11… number sentences intended for practice in Therefore, the sum of any two even numbers the development of a skill. is 2. POLYA’S FOUR-STEP PROCESS The most famous study of problem-solving ROUTINE AND NON-ROUTINE PROBLEM: techniques were developed by George Polya. Routine Problem - using at least one of the four arithmetic operations and/or ratio to He is known to be “The Father of solve problems. Problem-Solving”. Non-routine Problem - may be solved in Steps in Problem-Solving: different ways or strategies and may have 1. Understand the problem (find it out) – more than one answer or solution. Read the problem, if possible, several times, and analyze it carefully. Communication Processes (e.g. 2. Devise a plan (think) – Devise what explaining, talking, agreeing, questioning) appropriate strategy to attack a problem. Reasoning Processes (e.g. analyzing, 3. Carry out the plan (solve it) – After knowing recognizing patterns, conjecturing, proving) a strategy, carry out your plan of attack. Recording Processes (e.g. drawing, writing, 4. Look back (verification) – Check if your listing, graphing, symbolizing) answer is reasonable. Operational Processes (e.g. data collecting, sorting, ordering, classifying) Justine Lansangan