4.2 blocking
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Researchers want to test if a new detergent works better in warm or cold water for hand-washing clothes. To do this, they will use a randomized block design where they block, or separate, the clothes into categories based on expected differences in how they respond to temperature (e.g. cotton vs. wool). They will then randomly assign the detergent and water temperatures within each block to get more precise conclusions about how the detergent performs in each temperature for each type of fabric.
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This document describes an experimental design submitted by Bimal Antony to Mr. Ajesh P. Joseph at Marian College. It provides an introduction to experimental design and research, outlining the purpose of experimental research to investigate cause-and-effect relationships. It then describes various categories of experimental design including before-after, after-only, quasi-experimental, completely randomized, randomized complete block, Latin square, factorial, and Solomon four-group designs. It concludes by discussing analysis of covariance and the advantages, disadvantages, and limitations of experimental methods.
Experimental Design.pptx
Experimental design involves establishing the relationship between independent and dependent variables through a controlled experiment. There are different types of experimental designs including pre-experimental, true experimental, and quasi-experimental. Pre-experimental designs lack control groups, true experiments use random assignment to control groups, and quasi-experiments do not use random assignment. Experimental designs specify the treatments or levels of independent variables to apply and how they are combined to answer the research objectives and hypotheses. The treatment design and levels are important for drawing valid conclusions from experimental results.
Niyati experimental designs
This document outlines different types of experimental research designs, including true experimental, quasi-experimental, and pre-experimental designs. It discusses key elements like manipulation of independent variables, use of control groups, and randomization. True experiments aim to establish causation and include random assignment to groups. Quasi-experiments are similar but lack random assignment or a control group. Pre-experiments are the weakest design with no control groups or random assignment. Several specific experimental designs are described like post-test only, pre-test post-test, and randomized block designs. Advantages and disadvantages of different designs are also presented.
2. experimental designs
This document discusses various experimental research designs, including pre-experimental, true experimental, and randomized controlled trial designs. It provides examples and descriptions of different types of designs, such as one-shot case design, one-group pretest-posttest design, post-test-only control design, pretest-post-test-only design, Solomon four-group design, factorial design, randomized block design, and crossover design. The goal is to help students understand how to properly structure experiments to minimize threats to validity and draw accurate conclusions about causal relationships between independent and dependent variables.
Experimental research
Experimental research uses manipulation of independent variables and random assignment to test hypotheses about cause-and-effect relationships between variables. Key characteristics include comparing experimental and control groups, where the experimental group receives the treatment and the control group does not. True experimental designs with random assignment are strongest, while quasi-experimental designs lack random assignment. Various designs are used including pretest-posttest and posttest-only with control groups. Factorial designs study interactions between treatments and moderator variables.
Experimental Design and its Types ppt
The document discusses experimental research design. It defines experimental research design as manipulating an independent variable to determine its causal effect on a dependent variable. It describes three types of experimental designs - true experiments where the researcher controls all variables, quasi-experiments where some control is given up for feasibility, and pre-experiments with no control over variables. Several specific experimental designs are also outlined, including post-test only, pre-test post-test, and randomized block designs. Key aspects of experiments like manipulation, control, and random selection are defined.
Experimental research Design
This document discusses different types of experimental research designs. There are three main types of designs - pre-experimental, quasi-experimental, and true experimental. True experimental designs have the highest degree of control and involve manipulating the independent variable, using a control group, and random assignment to control for extraneous variables. Quasi-experimental designs manipulate the independent variable but lack either random assignment or a control group. Pre-experimental designs have the least control and do not use control groups or random assignment. Several examples of designs are provided within each category.
Adler clark 4e ppt 08
The document discusses experimental research designs. It defines key terms like causal hypotheses, experimental designs, control groups, stimuli, placebos, internal validity, and pretest-posttest designs. It describes classic experimental designs that involve randomly assigning subjects to experimental and control groups, introducing/manipulating the independent variable between pre- and posttests, and comparing results. Threats to internal validity like maturation and history are also outlined.
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Bioequivalence experimental study design By Vishnu Datta M
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This document discusses the odds of a successful outcome of an event. It defines odds as the ratio of the probability of success to the probability of failure. It also notes that probability is a ratio of the desired outcome to the total possible outcomes. The document directs the reader to additional examples on specific pages to further illustrate the concept of odds and probability.
Logarithmic transformations
1. The document discusses logarithmic transformations, which can be used to transform non-linear data into a linear format to better model exponential relationships.
2. There are two options for logarithmic transformations: taking the log of just the response variable y, or taking the log of both the explanatory variable x and the response variable y.
3. Graphing the transformed data allows one to determine which option produces a more linear relationship and thus the better transformation to use.
Transforming data for inference
Transforming data involves changing the scale of measurement used to collect data in order to better model relationships between variables. For example, when modeling the relationship between length and weight of fish, a power model using length cubed better fits the data than a linear model, since weight is related to the cube of length. The marine lab provided reference data that was used to find the least-squares regression line equation to predict fish weights based on their lengths.
Inference in regression line test
This document discusses regression inference and significance testing for the slope parameter in a simple linear regression model. It provides details on conducting a t-test for the slope of the population regression line to test if the slope is equal to some hypothesized value (the null hypothesis) versus an alternative hypothesis where the slope is greater than, less than, or not equal to the hypothesized value. Examples are given on interpreting test statistics and p-values and drawing conclusions about the evidence for or against a linear relationship between two variables based on the regression analysis and test results.
Regression inference confidence intervals
This document discusses regression analysis and the conditions needed to perform statistical inference on regression models. It summarizes key concepts like the population regression line, sample regression line, residuals, and sampling distributions. It then describes 5 conditions for regression inference: linearity, independence, normality, equal variance, and randomness. An example is provided of students collecting data by dropping helicopters from various heights and analyzing the results. Steps are outlined to check if the conditions are met and interpret results from the computer output and confidence interval.
Using chi square wisely
The document discusses using chi-square tests to analyze categorical data. It explains that a chi-square test for homogeneity determines if variables are distributed the same across groups, while a chi-square test for association determines if variables are associated. The key is to select the right test based on whether data came from independent samples or a single sample classified on two variables. An example analyzes data on gender and fear from scary movies using a chi-square test for association.
Chi square test for homgeneity
This document discusses conditions for performing a chi-square goodness-of-fit test and chi-square test for homogeneity. The chi-square goodness-of-fit test requires that all expected counts be greater than 5. The chi-square test for homogeneity can be used to compare distributions across multiple groups, requires random sampling and independence of observations, and that expected counts be at least 5. The document provides an example comparing wine purchases with different music, calculates the chi-square statistic, and interprets the p-value to determine if distributions differ.
Relationships across distribution
The document describes a study conducted in a supermarket to examine the effect of background music on wine purchases. Customers were randomly assigned to one of three conditions: no music, French accordion music, or Italian string music. The number of bottles of French, Italian, and other wines purchased under each condition were recorded. A chi-square test is proposed to determine if the distribution of wine purchases differs across the three music treatments.
Chi square goodness of fit test
The document describes using a chi-square test to determine if the number of cars choosing each of three toll booth lanes is equal. An toll collector randomly sampled cars and recorded the lane chosen. The appropriate hypothesis is that the proportions of cars choosing each lane are equal (option c). The document then provides an example chi-square test on birth data to see if births are evenly distributed by day of week. It describes the chi-square test steps and conditions.
Chi square distribution table c
This document discusses using the chi-square statistic to test whether observed sample results differ significantly from expected results. It provides the formula for calculating chi-square and explains that larger chi-square values provide stronger evidence against the null hypothesis. It then works through an example using data on M&M colors to calculate chi-square and determine the P-value to assess significance. The key steps are outlined as calculating chi-square, determining the degrees of freedom, using a chi-square table to find the P-value based on the chi-square value and degrees of freedom, and comparing the P-value to the significance level to determine whether to reject or fail to reject the null hypothesis.
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This document discusses using a chi-square goodness-of-fit test to determine if the observed color distribution of M&M's in a sample package matches the expected distribution claimed by the company. Students are instructed to calculate expected counts for each color based on the company's proportions, observe counts in their package, and calculate a chi-square statistic by summing the squared differences between observed and expected counts divided by the expected counts. A large chi-square value provides evidence against the null hypothesis that the company's claimed color distribution is correct.
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This document provides instructions for long hand polynomial division. It explains that polynomial division involves splitting up the dividend polynomial and dividing it by the divisor polynomial. It demonstrates the step-by-step process of long hand division with an example of (m^2 - 7m - 11) / (m - 8). The key steps are multiplying the divisor by terms of the dividend and subtracting to cancel out terms until a remainder is reached.
Solving triangles pp slides
This document contains two triangle problems. The first asks to find angle E given that f = 17 and d = 32. The second asks to find angle H given that g = 28 and h = 21.
Conditional prob & independence
1) The document discusses conditional probability and independence using examples of students with pierced ears and their gender.
2) Conditional probability is defined as P(B|A), the probability of event B occurring given that event A has already occurred.
3) The example shows that the probability of a student being male given they have pierced ears is different than the probability of being male in general, indicating the events are dependent.
Two way tables & venn diagrams
Students in a college statistics class collected data on 178 students' gender and whether their ears were pierced. They recorded this information in a two-way table to calculate probabilities. The two-way table showed that of the 90 male students, 19 had pierced ears. Of the 88 female students, 84 had pierced ears. The probabilities of being male with pierced ears, having pierced ears, or being male or having pierced ears can be determined from this two-way table.
Probability models & basic rules
This document provides an overview of key concepts in probability models, including:
1. A probability model consists of a sample space (the set of all possible outcomes) and probabilities assigned to each outcome. Common examples are coin tosses and dice rolls.
2. Events are subsets of outcomes from the sample space. The probability of an event, written P(A), is the chance it occurs.
3. For mutually exclusive events like getting a sum of 5 or 6 on dice, the total probability is the sum of the individual probabilities. Basic rules of probability include that all probabilities must sum to 1 and the probability of an event's complement is 1 minus the original probability.
Simulation
The document discusses simulations and provides examples. It describes simulating a parking lottery at a high school to determine if it was plausibly fair after two students from the same statistics class won spots. It outlines planning the simulation by numbering all 95 eligible students, those in statistics 1-28 and others 29-95, then randomly selecting two numbers using a random number table to simulate drawing winning tickets. The document also discusses simulating collecting NASCAR trading cards from cereal boxes to determine if a fan should be surprised it took 23 boxes to collect all 5 cards.
Mthys of probability
This document discusses key concepts in probability, including examples using dice, coin tosses, and the law of large numbers. It explains that the proportion of specific outcomes approaches a single probability value as the number of repetitions increases. Probability is defined as a value between 0 and 1 describing the long-run frequency of an outcome. Myths about probability, like short-run predictability and the law of averages, are also addressed.
4.3 using studies wisely
The document discusses different types of studies and the inferences that can be drawn from them. Random selection of individuals allows inferences about a population, while random assignment to groups allows inferences about causation. Observational studies using non-randomly assigned groups can infer characteristics of populations but not causation. Only randomized experiments and observational studies using random selection can potentially establish causal relationships.
4.2 placebos & double blind
This document describes an experiment conducted by an electric company to test two approaches to encouraging energy conservation among customers: 1) providing small digital displays in households to show current electricity use and estimated monthly cost, and 2) giving customers a chart and information to monitor electricity use from their outside meter. The company will conduct an experiment comparing these two approaches to a control group that only receives general energy consumption information. The summary describes how the experiment would use a randomized design involving 60 households willing to participate, with the households randomly assigned to the three groups to compare the approaches.
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4.2 blocking
- 1. Blocking Just as in sampling, when a population consisted of groups of individuals that were “similar within but different between”, a stratified random sample gives a better estimate than a simple random sample Blocking is used to separate the experimental units into categories before an experiment based on some way that is expected to effect the response to the treatments Researchers want to determine if a new detergent for clothes that require Hand-Washing works better in warm water and cold water
- 2. Blocking Researchers want to determine if a new detergent for clothes that require Hand-Washing works better in warm water and cold water. Develop an experimental design using Blocking: Randomized Block Design – the random assignment of experimental units to treatments is carried out separately within each block.
- 3. Blocking Blocking allows us to draw separate conclusions about each block, thus providing more precise overall conclusions A wise experiment will develop blocks based on the most important unavoidable sources of variability among the experimental units - randomization will balance out effects of remaining lurking variables and allow unbiased comparison of treatments ** Control what you can, block what you can’t control, and randomize to create comparable groups.
- 4. Blocking Women and men respond differently to advertising (think about car advertisements). Researchers want to design an experiment that compares the effectiveness of three different advertisements for the same product. a. Why might a randomized block design be preferable to a completely randomized design. b. Outline a randomized block design using 300 volunteers (180 men & 120 women) as subjects. Describe how you would carry out the random assignment required by your design.
- 5. Blocking Matched Pairs – special cases of a randomized block designs using blocks of size 2 - Create blocks of matching pairs of experimental units (2 that are similar) - Randomly assign one treatment to each member of the pair Sometimes a “pair” can be just one experimental unit that gets both treatments, one after the other. The randomization is the order of the treatments as that may influence the response.
- 6. Blocking Type of design determines the method of analysis: Completely randomized design – compare measures of two groups (pulse rate stand vs. sit) Matched Pairs Design – each unit is a block, compare effects of treatments within each block by examining differences (stand vs. sit)