Figure 12.4: e – log sv’ plot 5. Recompression Index. Determine it from the plot of voids ratio (e) vs log σ plot as (Figure 12.4 or 12.5) 12.11 6. Coefficient of compressibility. It is calculated as follows av = 0.435 Cc/Avg. pressure for the increment 12.12 where Cc = compression index av = Coefficient of compressibility
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Table 2A and 3B 3A) we performed A priori chi-square test for these two tables. The main reason for the chi-square is to find out if the expected value is any different from the observed value. This part of the observation tested the null hypothesis‚ which states that‚ if when Ear#1(which was test-crossed kernel) is counted and approves of the Mendelian expectation of 1:1:1:1 phenotypic ratio. The chi-square for this data was excepted to accept the null hypothesis. The reason that it was expected
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Probability 2 Theory Probability theory is the branch of mathematics concerned with probability‚ the analysis of random phenomena. (Feller‚ 1966) One object of probability theory is random variables. An individual coin toss would be considered to be a random variable. I predict if the coin is tossed repeatedly many times the sequence of it landing on either heads or tails will be about even. Experiment The Experiment we conducted was for ten students to flip a coin one hundred times
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Phuong Pham Prof. Johnson Lab 3 Gravimetric determination of Calcium as CaC2O4.H2O Feb‚ 27‚ 2014 I. Objective: The purpose of this lab is to determine how well gravimetric method measures the calcium level in a solution of known calcium concentration. II. Method: a) Overview: In this lab we will prepare 25 mL known concentration Calcium solutions. Then we will use Gravimetric method to determine the concentration of Calcium in each solution to figure out how well this method is. As we know
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We calculated mean scores for each ATIEIS statement (Table 3). Mean scores between 1 and 3 on ATIEIS were considered to indicate a negative attitude while those above 3 indicated a positive attitude to inclusion. While calculating mean score‚ responses to negative statements were converted to positive (Disagreeing to a negative sentence is a positive). T-tests were conducted to find the significance of differences in mean scores across demographic variables. Analysis of means (depicted in brackets)
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3.6.2 Correlation Analysis Correlation analysis is one of the data analysis method use in quantitative research when researchers intended to examine the relationship between both variables (Research Methodology‚ n.d.). The Pearson’s Moment Correlation Coefficient (PMCC) ranges between +1 and -1.If the coefficient value is +1 and -1‚ it is an indication that the relationships between both variables are strong. A value of +1 indicate that there is a positive relationship between variables‚ means that
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Figure 1: The pupillary reflex data demonstrates the mean change in each pupil’s diameter. In this experiment‚ data was taken from six groups. From each group‚ 3 things were measured and averaged together and that was Ambient‚ Both Eyes Open and One Eye Open. The vertical lines represents the standard deviation of the averages through error bars. The height represent the average pupil diameter amongst the Ambient‚ Both Eyes Open and One Eye Open data which was measured in centimeters (cm). 2) The
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Chi-square requires that you use numerical values‚ not percentages or ratios. Then calculate 2 using this formula‚ as shown in Table B.1. Note that we get a value of 2.668 for 2. But what does this number mean? Here’s how to interpret the 2 value: 1. Determine degrees of freedom (df). Degrees of freedom can be calculated as the number of categories in the problem minus 1. In our example‚ there are two categories (green and yellow); therefore‚ there is I degree of freedom. 2. Determine a relative
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DATA ANALYSIS Population Size = 9 Samples from Unilever = 5 Sample from Procter & Gamble = 4 SAMPLE ANALYSIS QUESTION NO. AGREE AGREE DISAGREE DISAGREE NEUTRAL NEUTRAL UNILEVER P & G UNILEVER P & G UNILEVER P & G 1 5 5 0 0 0 0 2 5 2 0 0 0 2 3 5 5 0 0 0 0 4 5 5 0 0 0 0 5 5 5 0 0 0 0 6 5 1 0 1 0 2 7 5 5 0 0 0 0 8 5 2 0 0 0 2 9 5 5 0 0 0 0 10 5 1 0 1 0 2 QUES. NO. 15 16 17 18 19 HIGH HIGH LOW LOW MODERATE MODERATE N/A N/A UNILEVER P & G UNILEVER P & G UNILEVER P & G UNILEVER P & G 5 3 0 0 0
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Figure 3.5 Remaining Service Life The figure 3.5 depicts PCR values generated by Kaplan Meier method. The graph of S(t) versus the t gives the Kaplan-Meier survival curves. Figure 3.6 Life Time of Roads The figure 3.6 depicts the Kaplan – Meier survival curves generated for each PCR threshold by using PCR data and is obtained by plotting the values given in the figure 3.5. S(t) Years 100-95 15-11 yrs 95-90 11-8 yrs 90-85 8-7 yrs 85-80 7-6 yrs 80-75 6-5 yrs 75-70 5-3yrs 70-65 3-1 yrs 65-60 <1
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