Descriptive Statistics. Our analysis focuses on the two main types on NTMs adopted by TTIP and TPP countries that affect trade flows, namely the SPS and TBT measures. We use the notifications made by these countries to the WTO.8 Each notification provides information on the notifying 8These notifications are used by the WTO in its 2012 World Trade Report (WTO, 2012) and are avail- able via the Integrated Trade Intelligence Portal (I-TIP) (xxxx://xxx.xxx.xxx/english/res_e/statis_e/ itip_e.htm). Product codes are often missing from the I-TIP database and were added at the HS 4-digit level by the Centre for WTO Studies of the Indian Institute of Foreign Trade (xxxx://xxxxxxxxx.xxxx.xx.xx/). country (the importer), the affected product (defined at the HS 4-digit level), and the type of measure (SPS vs. TBT). We include all measures notified up to the end of 2012 which means that our dataset is more up to date than that developed by Xxx et al. (2009) which was the basis for several previous studies.9 However, WTO members are required to notify only new or changed measures, and the notification requirements apply only to measures which differ from international standards, guidelines, or recommendations, or to situations where no standards exist, and, in addition may have a significant impact on trade. As pointed out in the literature, this could affect the results of an analysis of their trade and welfare impacts. Before we present our descriptive statistics, recall that in almost all cases, NTMs are unilateral measures, i.e. they apply to a given product regardless of its origin. Furthermore, the principle of mutual recognition applies among EU Member States. According to this principle, goods and services can move freely across Member States, and national legislation does not have to be harmonized. Therefore, to avoid bias, we exclude intra-EU trade flows from our NTMs analysis. Table 4 provides some statistics on the share of agri-food and non agri-food products (defined at the 6-digit level of the HS classification) affected by at least one NTM, in the US, EU, and TPP countries other than the US. These statistics are further broken down into SPS and TBT measures. A very large share of products is affected by NTMs in these markets; however, our results suggest some differences between agri-food and non agri-food products. TTIP and TPP countries notify SPS and TBT measures on almost all agri-food products. For non agri-food products, the picture is different. For instance, the US not...
Descriptive Statistics. To examine the characteristics of the data used in the study, descriptive statistics were computed for individual research variables and the result are relayed on Table 1: Table 1: Summary of Descriptive Statistics LRGDP LRGDPIS LINSTA LINSTC Mean 10.90194 5.417658 13.25624 11.56805 Median 10.98033 5.508221 13.28180 11.68400 Maximum 11.17588 5.695592 14.51741 14.03675 Minimum 10.40441 4.857950 11.73019 9.150081 Std. Dev. 0.259721 0.250366 0.772570 1.651455 Skewness -0.580030 -1.012641 -0.453557 -0.027450 Kurtosis 1.903908 2.804567 2.623751 1.569840 Xxxxxx-Xxxx 1.910366 3.104972 0.723315 1.536279 Probability 0.384742 0.211721 0.696521 0.463875 Sum 196.2349 97.51784 238.6124 208.2249 Sum Sq. Dev. 1.146735 1.065610 10.14670 46.36416 Observations 18 18 18 18 Source: Authors’ Computation, 2022 Table 1 describes the characteristic nature of the sample data series relating to the effect of insurance sector development and the growth of Nigerian economy. Thus, is obvious from the Table 1 that insurance total assets (INSTA) has highest average value of 13.26, this is followed by insurance sector total claims paid (INSTC) with mean value of 11.57. The next is economic growth (RGDP)with average value of 10.90 while the productivity of the insurance sector (RGDPIS) has the lowest average value of
Descriptive Statistics. From the table above show the mean score from post-test in the experimental class (56.62). It has been shown that using the climbing grammar mountain game can help students improve their scores. to see if the climbing grammar mountain game improves students' writing in recount texts and whether climbing grammar mountain has a significant impact on students' writing skills, using Paired Sample Test and Independent Sample Test, the researcher compared the means of one variable for two groups of cases using IBM SPSS Statistic 25.
Descriptive Statistics. We start the analysis by summarizing, in Table 2, the median, mean, and standard deviations of the order levels in each of our treatments, and comparing them to theoretical benchmarks. Time Horizon Bonus 2 8 Data (50 de- cisions) Theory p-value* Data (50 de- cisions) Data (12 deci- sions) Theory p-value* p-value** Median 51.44 50.91 50.58 0 Mean 52.31 50 0.1354 51.53 52.29 50 0.1375 0.7061 Std Dev (4.63) (5.16) (6.01) Median 56.00 60.20 61.79 5 Mean 58.55 54 0.0217 62.78 63.87 51 0.0005 0.4328 Std Dev (8.04) (11.65) (13.92) Median 68.57 79.66 76.75 25 Mean 68.19 70 0.1968 79.04 76.64 86 0.0002 0.0001 Std Dev (8.45) (7.54) (7.70) Median 81.92 86.58 85.63 50 Mean 81.72 0.0003 86.03 83.47 0.0415 0.0425 Std Dev (8.43) 92 (4.70) (6.94) 88 50 (Exec) Median Mean Std Dev 83.70 83.24 (9.55) 0.0391 82.24 81.65 (3.97) 76.71 75.77 (7.30) 0.0039 0.9314 * Wilcoxon Signed-Rank Test, Ho: Median Order = Theoretical Prediction ** Xxxx-Xxxxxxx Test, Ho: Median order for T=2 condition = Median order for T=8 condition Table 2. Summary of the average and median order-up-to levels and their standard deviations in all treatments, as well as corresponding theoretical benchmarks and results of hypothesis tests. P-values below 0.05 are bold. Since in the T = 2 condition participants observed 100 periods and in the T = 8 condition 400, we also report descriptive statistics in the T = 8 treatments for the first 12 decisions (corresponding to the first 96 of the 400 periods)5. All comparisons we report in this section use median orders for individual subjects as the unit of analysis and all 50 decisions in the T = 8 condition. All p- values we report below are 2-sided. For one-sample tests comparing median orders to their theo- retical benchmarks, reported in Table 2, we use the Wilcoxon Signed-Rank Test. For two- sample tests we use the Xxxx-Xxxxxxx Test. Table 3 shows expected profits, standard devia- tions of profit and probabilities of meeting the target level for optimal order levels as well as av- 5 The median order-up-to levels for the first 12 decisions in the T=8 condition are very similar to the medians for all 50 decisions, and standard deviations are generally higher. Using the 12-decision data in subsequent analysis makes no difference to any of the statistical comparisons we report, with one exception (see result 6 below). The difference between the orders in the B=50 condition is significant when all 50 decisions are used, but is not significant when only the first 12 de...
Descriptive Statistics. Table 1(a) to Table 1(d) show the descriptive statistics of the model variables in the LME model fitting datasets of 2013, 2014, 2015, and overall, respectively. A total of 11,586, 12,741, and 16,883 data records were included in the 2013, 2014, and 2015 model fitting dataset, respectively. The overall dataset covers 1095 sample days (from January 1, 2012 to December 31, 2015). Within this time interval, the overall mean PM 2.5 concentration was 69.29 μg/m3, and the mean values of AOD was 0.58. The year specific mean PM 2.5 concentrations were 83.80 μg/m3, 68.26 μg/m3, and 60.12 μg/m3 for 2013, 2014, and 2015, respectively. The corresponding annual mean AOD values were 0.64, 0.60, and 0.54,respectively. The annual average PM 2.5 concentrations show a decreasing trend from 2013 to 2015. The seasonal mean PM2.5 concentration was highest in winter and lowest in summer (Table 2(a) to Table 2(d)), which was consistent with previous study.(Lv, Hu, Xxxxx, Xxxxxxx, & Xxx, 2016) The highest mean AOD was in summer and the lowest in fall. The seasonal patterns of PRECTOT, PBLH, RH_PBLH and NDVI were similar that the highest value occurred in summer and the lowest in winter. The seasonal patterns of PM2.5 and AOD were different. The relationship between PM2.5 and AOD is complex, which can be strongly affected by geographical, meteorological, and seasonal conditions. (Xxxx Xxx, Xxxxxxxx, Xxxx, & Xxxxxxxxx, 2007)
Descriptive Statistics. The average age for participant was 24 with ages ranging from 16 to 41. The average age of their infant was four months, ranging from 11 days to 12 months. Fifty-five percent of the participants (n=11) attended secondary school, defined as grades seven through eleven and one participant reported attending college but not completing her degree. Seventy-five percent (n=15) of the mothers reported being unemployed at the time of the interview. Of the five that were employed, two were working in the recycling plant in La Chureca and three worked as small- scale vendors, selling items like beans and hand-crafted goods. One of the vendors, reported that she was part of a micro-loan cooperative program previously set up by MPI but now independent from the organization. Five were single at the time of the interview, and the remaining 15 reported either being married (n=4) or partnered/in a relationship (n=11). The average number of children among the participants was 2.3, with number of children ranging from one to seven. Five of the mothers were first time mothers, eight mothers had two children, and six had three. The average number of people living in each household was 8.1, including both family and non- family members. The reported average monthly income was $3,315 Cordobas, equaling to $106.25 in U.S. dollars. Fourteen mothers reported spending money on powered formula and monthly average spent on powdered formula among these mothers was $561.43 Cordobas, equaling to $17.97 U.S dollars. Monthly money spent on formula among the 14 mothers ranged from $120 to $1,040 Cordobas. Themes Exclusive Breastfeeding Out of all 20 mothers interviewed, only two, Xxxxxxx and Xxxxx, reported practicing exclusive breastfeeding. At the time of the interview, Xxxxxxx’x infant was 9 months old and Karen’s child was 12 months old. All 18 mothers, even those with infants younger than six months reported having already introduced foods or substances at the time of the interview. Ten of the mothers reported only feeding their infant breast milk for the first three days. For these moms, reasoning and the timeframe for the introduction of other substances like water, and food varied. Participants were asked about the term “lactancia materna exclusiva” or “exclusive breastfeeding,” and the majority of the moms (n=15) did not know the definition or had not heard the term before, including the two who were actually practicing exclusive breastfeeding. However, unawareness of the term...
Descriptive Statistics. Descriptive statistics for all of the continuous variables used in the analyses are shown in Table 2.1 below. All continuous variables were log-transformed for the correlation and multiple linear regression analyses except ‘last filter backwash’ since log transformation did not yield a normal distribution. Samples from all 26 pools were analyzed for most of the microorganisms. Surface P. aeruginosa has the smallest sample size (n=15) followed by surface Staphylococcus spp. (n=24). The highest mean concentrations were bulk water HPC-PCA (mean: 139.42 CFU/100 ml; range: 4 - 601 CFU/100 ml) and bulk water HPC-R2A (mean: 136.23 CFU/100 ml; range: 3 - 705 CFU/100 ml). The lowest mean concentration was surface P. aeruginosa (mean: 0.93 CFU/100 ml; range: 0 - 13 CFU/100 ml) and bulk water total coliforms (mean: 3.01 CFU/100 ml; range: 0 - 73 CFU/100 ml). For pool staff practices, the average number of days between pool vacuuming was 14.22 days (range: 1 - 90 days). The average time since the last pool vacuuming was 9.31 days (range: 0 - 46 days). The average number of days between filter backwashing was 9.15 (range: 1 - 60 days). The average time since the last filter backwash was 4.80 days (range: 0 - 30 days). Descriptive statistics for the categorical variables are shown in Table 2.2 below. The majority of the pools measured chlorine every 1 hour (72%) and pH every 1 hour (68%) versus every 2 hours. For the observed swimmer behaviors, 36% of the pools reported seeing children wearing swim diapers in the pool all the time. Pre-swim showering and seeing dirt in the pool were each reported by 20% of the pools as occurring all the time. Seeing people eating or drinking near the pool was reported by 16% of the pools as occurring all the time. Diaper changing near the pool was reported by 0% of the pools as occurring all the time.
Descriptive Statistics. Summary statistics on the socio-demographic and economic background of our subject pool are presented in Table 1. The objective of collecting such information is to investigate potential heterogeneous effects of experimental treatments but also to check the validity of the randomized allocation procedure to the different experimental treatments. Apart from a higher proportion of students in the IES treatment (27% versus 14% in the PES treatment, t- stat of 3.1) and a larger average household size in the PES treatments (4.7 versus 4.2 in the IES treatment, t-stat of 1.9), covariates are well balanced across the experimental treatments.xiii Despite randomizing the assignment of treatments to sessions, initial trust and trustworthiness turned out to be significantly higher in the IES than in the PES treatment. Trust is measured as the amount sent and trustworthiness as the average amount returned as percentage of the amount sent, averaged over all the possible amounts sent (elicited with the strategy method). Subjects assigned to the PES and IES treatments sent on average 5.2 and
Descriptive Statistics. At the time of this analysis, there were only 100 women (of the 181 enrolled) whom we had collected the appropriate data on BP, PM2.5, age, and BMI. 74% participants were over-weight or obese and the mean BMI was 26.6 kg/m2, indicating an average of overweight status in our sample [32]. The mean age was 48 years old and a large majority of participants had SBP and DBP which were within the normal blood pressure range. 94% had SBP less than or equal to 120 mmHg and 92% had DBP less than 80 mmHg. This was expected since the study excluded hypertensive women. At most 19 of the 569 (0.03%) women assessed for eligibility were excluded due to hypertension. Indoor PM2.5 exposure was very high, with an average of 128.83 μg/m3. 87% of participants had an exposure above the WHO’s 24-hour average limit of 25 μg/m3 [20]. Below is a summary of descriptive statistics of the sample of 100 women: Table 1. Summary of Descriptive Statistics for n=100 Sample PM2.5 (μg/m3) SBP (mmHg) DBP (mmHg) Age (Years) BMI (kg/m2) Time BP taken (hour since 00:00) Minimum 3.99 71.50 44.50 24.00 17.74 5.90 Median 69.01 100.00 67.00 48.50 25.97 7.63 Maximum 1155.49 134.00 92.00 64.00 36.02 11.02 IQR 91.21 14.00 11.00 16.00 5.48 1.60 Mean 128.83 100.00 66.76 47.85 26.60 7.69 SD 188.71 12.20 8.91 10.43 3.87 1.09
Descriptive Statistics. The researcher used data received from OSIIS for the 2008 two-year old children to run a secondary data analysis of the immunization coverage rates. Statistical Analysis Systems (SAS) was used for the analysis. The following variables were analyzed from the data to provide a snap shot of coverage by race, county vaccination coverage rates and gender. Ethical Issues The study did not required direct contact with children, their families, or providers. All immunization history was extracted from the immunization registry for the secondary data analysis.
